{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/semantic-similarity/papers/3","list_of":"/task/semantic-similarity","task":"Semantic Similarity","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":3,"pages_in_order":16,"rows_per_page":100,"rows":[201,300],"of":1564,"counts":{"archive_papers_tagged":1564,"with_a_code_link":534,"where_syntology_ran_a_sample":102,"not_listed_spam_title":0,"listed":1564,"listed_where_code_ran":102,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":85,"every_run_a_failure_of_syntologys_instrument":17,"listed_with_a_run_with_no_instrument_failure":85,"listed_every_run_a_failure_of_syntologys_instrument":17,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/semantic-similarity","prev":"/task/semantic-similarity/papers/2","next":"/task/semantic-similarity/papers/4","papers":[{"url":"/paper/incorporating-judgment-prediction-into-legal","slug":"incorporating-judgment-prediction-into-legal","title":"Explicitly Integrating Judgment Prediction with Legal Document Retrieval: A Law-Guided Generative Approach","date":"2023-12-15","arxiv_id":"2312.09591","repositories_listed":1,"syntology":null},{"url":"/paper/fedssa-semantic-similarity-based-aggregation","slug":"fedssa-semantic-similarity-based-aggregation","title":"FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning","date":"2023-12-14","arxiv_id":"2312.09006","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/fedssa-semantic-similarity-based-aggregation#ran","syntology_url":"https://syntology.ai/paper/2312.09006","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.09006"}},"official":{"repos":["lipingyi/fedssa"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/tap4llm-table-provider-on-sampling-augmenting","slug":"tap4llm-table-provider-on-sampling-augmenting","title":"TAP4LLM: Table Provider on Sampling, Augmenting, and Packing Semi-structured Data for Large Language Model Reasoning","date":"2023-12-14","arxiv_id":"2312.09039","repositories_listed":1,"syntology":{"n":14,"n_ran":13,"n_constructed":0,"n_ran_checked":13,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":12,"n_pointer_only":14,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 1 honoured, 0 violated, 12 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/tap4llm-table-provider-on-sampling-augmenting#ran","syntology_url":"https://syntology.ai/paper/2312.09039","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.09039"}},"official":null}},{"url":"/paper/encoding-surgical-videos-as-latent","slug":"encoding-surgical-videos-as-latent","title":"Encoding Surgical Videos as Latent Spatiotemporal Graphs for Object and Anatomy-Driven Reasoning","date":"2023-12-11","arxiv_id":"2312.06829","repositories_listed":1,"syntology":null},{"url":"/paper/mining-gaze-for-contrastive-learning-toward","slug":"mining-gaze-for-contrastive-learning-toward","title":"Mining Gaze for Contrastive Learning toward Computer-Assisted Diagnosis","date":"2023-12-11","arxiv_id":"2312.06069","repositories_listed":1,"syntology":null},{"url":"/paper/few-shot-class-incremental-learning-via-3","slug":"few-shot-class-incremental-learning-via-3","title":"Few-Shot Class-Incremental Learning via Training-Free Prototype Calibration","date":"2023-12-08","arxiv_id":"2312.05229","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/few-shot-class-incremental-learning-via-3#ran","syntology_url":"https://syntology.ai/paper/2312.05229","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.05229"}},"official":{"repos":["wangkiw/teen"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/hyperpolyglot-llms-cross-lingual","slug":"hyperpolyglot-llms-cross-lingual","title":"Hyperpolyglot LLMs: Cross-Lingual Interpretability in Token Embeddings","date":"2023-11-29","arxiv_id":"2311.18034","repositories_listed":1,"syntology":null},{"url":"/paper/reinforcement-replaces-supervision-query","slug":"reinforcement-replaces-supervision-query","title":"Reinforcement Replaces Supervision: Query focused Summarization using Deep Reinforcement Learning","date":"2023-11-29","arxiv_id":"2311.17514","repositories_listed":1,"syntology":null},{"url":"/paper/autokg-efficient-automated-knowledge-graph","slug":"autokg-efficient-automated-knowledge-graph","title":"AutoKG: Efficient Automated Knowledge Graph Generation for Language Models","date":"2023-11-22","arxiv_id":"2311.14740","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/autokg-efficient-automated-knowledge-graph#ran","syntology_url":"https://syntology.ai/paper/2311.14740","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.14740"}},"official":{"repos":["wispcarey/autokg"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/one-size-fits-all-for-semantic-shifts","slug":"one-size-fits-all-for-semantic-shifts","title":"One Size Fits All for Semantic Shifts: Adaptive Prompt Tuning for Continual Learning","date":"2023-11-18","arxiv_id":"2311.12048","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":1,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/one-size-fits-all-for-semantic-shifts#ran","syntology_url":"https://syntology.ai/paper/2311.12048","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.12048"}},"official":{"repos":["kaist-dmlab/adapromptcl"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/eval-gcsc-a-new-metric-for-evaluating-chatgpt","slug":"eval-gcsc-a-new-metric-for-evaluating-chatgpt","title":"Eval-GCSC: A New Metric for Evaluating ChatGPT's Performance in Chinese Spelling Correction","date":"2023-11-14","arxiv_id":"2311.08219","repositories_listed":1,"syntology":null},{"url":"/paper/sub-sentence-encoder-contrastive-learning-of","slug":"sub-sentence-encoder-contrastive-learning-of","title":"Sub-Sentence Encoder: Contrastive Learning of Propositional Semantic Representations","date":"2023-11-07","arxiv_id":"2311.04335","repositories_listed":1,"syntology":null},{"url":"/paper/tpsence-towards-artifact-free-realistic-rain","slug":"tpsence-towards-artifact-free-realistic-rain","title":"TPSeNCE: Towards Artifact-Free Realistic Rain Generation for Deraining and Object Detection in Rain","date":"2023-11-01","arxiv_id":"2311.00660","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":2,"n_instrument":5,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":8,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 5 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/tpsence-towards-artifact-free-realistic-rain#ran","syntology_url":"https://syntology.ai/paper/2311.00660","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.00660"}},"official":{"repos":["shenzheng2000/tpsence"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/seine-short-to-long-video-diffusion-model-for","slug":"seine-short-to-long-video-diffusion-model-for","title":"SEINE: Short-to-Long Video Diffusion Model for Generative Transition and Prediction","date":"2023-10-31","arxiv_id":"2310.20700","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/seine-short-to-long-video-diffusion-model-for#ran","syntology_url":"https://syntology.ai/paper/2310.20700","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.20700"}},"official":null}},{"url":"/paper/few-shot-hybrid-domain-adaptation-of-image","slug":"few-shot-hybrid-domain-adaptation-of-image","title":"Few-shot Hybrid Domain Adaptation of Image Generators","date":"2023-10-30","arxiv_id":"2310.19378","repositories_listed":1,"syntology":null},{"url":"/paper/the-distributional-hypothesis-does-not-fully","slug":"the-distributional-hypothesis-does-not-fully","title":"The Distributional Hypothesis Does Not Fully Explain the Benefits of Masked Language Model Pretraining","date":"2023-10-25","arxiv_id":"2310.16261","repositories_listed":1,"syntology":null},{"url":"/paper/topology-aware-debiased-self-supervised-graph","slug":"topology-aware-debiased-self-supervised-graph","title":"Topology-aware Debiased Self-supervised Graph Learning for Recommendation","date":"2023-10-24","arxiv_id":"2310.15858","repositories_listed":1,"syntology":null},{"url":"/paper/meaning-representations-from-trajectories-in","slug":"meaning-representations-from-trajectories-in","title":"Meaning Representations from Trajectories in Autoregressive Models","date":"2023-10-23","arxiv_id":"2310.18348","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/meaning-representations-from-trajectories-in#ran","syntology_url":"https://syntology.ai/paper/2310.18348","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.18348"}},"official":{"repos":["tianyu139/meaning-as-trajectories"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/why-should-i-review-this-paper-unifying","slug":"why-should-i-review-this-paper-unifying","title":"Chain-of-Factors Paper-Reviewer Matching","date":"2023-10-23","arxiv_id":"2310.14483","repositories_listed":1,"syntology":null},{"url":"/paper/prompt-based-grouping-transformer-for-nucleus","slug":"prompt-based-grouping-transformer-for-nucleus","title":"Prompt-based Grouping Transformer for Nucleus Detection and Classification","date":"2023-10-22","arxiv_id":"2310.14176","repositories_listed":1,"syntology":null},{"url":"/paper/improving-long-document-topic-segmentation","slug":"improving-long-document-topic-segmentation","title":"Improving Long Document Topic Segmentation Models With Enhanced Coherence Modeling","date":"2023-10-18","arxiv_id":"2310.11772","repositories_listed":1,"syntology":null},{"url":"/paper/zero-shot-object-goal-visual-navigation-with","slug":"zero-shot-object-goal-visual-navigation-with","title":"Zero-Shot Object Goal Visual Navigation With Class-Independent Relationship Network","date":"2023-10-15","arxiv_id":"2310.09883","repositories_listed":1,"syntology":null},{"url":"/paper/context-compression-for-auto-regressive","slug":"context-compression-for-auto-regressive","title":"Context Compression for Auto-regressive Transformers with Sentinel Tokens","date":"2023-10-12","arxiv_id":"2310.08152","repositories_listed":1,"syntology":null},{"url":"/paper/transformers-for-green-semantic-communication","slug":"transformers-for-green-semantic-communication","title":"Transformers for Green Semantic Communication: Less Energy, More Semantics","date":"2023-10-11","arxiv_id":"2310.07592","repositories_listed":1,"syntology":null},{"url":"/paper/astroclip-cross-modal-pre-training-for","slug":"astroclip-cross-modal-pre-training-for","title":"AstroCLIP: A Cross-Modal Foundation Model for Galaxies","date":"2023-10-04","arxiv_id":"2310.03024","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":1,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/astroclip-cross-modal-pre-training-for#ran","syntology_url":"https://syntology.ai/paper/2310.03024","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.03024"}},"official":{"repos":["PolymathicAI/AstroCLIP"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/bridging-the-gap-between-structural-and","slug":"bridging-the-gap-between-structural-and","title":"Bridging the Gap between Structural and Semantic Similarity in Diverse Planning","date":"2023-10-02","arxiv_id":"2310.01520","repositories_listed":1,"syntology":null},{"url":"/paper/semantic-similarity-prediction-is-better-than","slug":"semantic-similarity-prediction-is-better-than","title":"Semantic similarity prediction is better than other semantic similarity measures","date":"2023-09-22","arxiv_id":"2309.12697","repositories_listed":1,"syntology":null},{"url":"/paper/instructerc-reforming-emotion-recognition-in","slug":"instructerc-reforming-emotion-recognition-in","title":"InstructERC: Reforming Emotion Recognition in Conversation with Multi-task Retrieval-Augmented Large Language Models","date":"2023-09-21","arxiv_id":"2309.11911","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/instructerc-reforming-emotion-recognition-in#ran","syntology_url":"https://syntology.ai/paper/2309.11911","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.11911"}},"official":{"repos":["LIN-SHANG/InstructERC"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/dual-modal-attention-enhanced-text-video","slug":"dual-modal-attention-enhanced-text-video","title":"Dual-Modal Attention-Enhanced Text-Video Retrieval with Triplet Partial Margin Contrastive Learning","date":"2023-09-20","arxiv_id":"2309.11082","repositories_listed":1,"syntology":null},{"url":"/paper/linktransformer-a-unified-package-for-record","slug":"linktransformer-a-unified-package-for-record","title":"LinkTransformer: A Unified Package for Record Linkage with Transformer Language Models","date":"2023-09-02","arxiv_id":"2309.00789","repositories_listed":1,"syntology":null},{"url":"/paper/calm-a-multi-task-benchmark-for-comprehensive","slug":"calm-a-multi-task-benchmark-for-comprehensive","title":"CALM : A Multi-task Benchmark for Comprehensive Assessment of Language Model Bias","date":"2023-08-24","arxiv_id":"2308.12539","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":2,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 2 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/calm-a-multi-task-benchmark-for-comprehensive#ran","syntology_url":"https://syntology.ai/paper/2308.12539","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.12539"}},"official":{"repos":["vipulgupta1011/calm"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/audio-visual-class-incremental-learning","slug":"audio-visual-class-incremental-learning","title":"Audio-Visual Class-Incremental Learning","date":"2023-08-21","arxiv_id":"2308.11073","repositories_listed":1,"syntology":{"n":10,"n_ran":7,"n_constructed":0,"n_ran_checked":2,"n_instrument":5,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":10,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 5 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/audio-visual-class-incremental-learning#ran","syntology_url":"https://syntology.ai/paper/2308.11073","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.11073"}},"official":{"repos":["weiguopian/av-cil_iccv2023"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/integrating-visual-and-semantic-similarity","slug":"integrating-visual-and-semantic-similarity","title":"Integrating Visual and Semantic Similarity Using Hierarchies for Image Retrieval","date":"2023-08-16","arxiv_id":"2308.08431","repositories_listed":1,"syntology":null},{"url":"/paper/semantic-similarity-loss-for-neural-source","slug":"semantic-similarity-loss-for-neural-source","title":"Semantic Similarity Loss for Neural Source Code Summarization","date":"2023-08-14","arxiv_id":"2308.07429","repositories_listed":1,"syntology":null},{"url":"/paper/beyond-semantics-learning-a-behavior","slug":"beyond-semantics-learning-a-behavior","title":"Beyond Semantics: Learning a Behavior Augmented Relevance Model with Self-supervised Learning","date":"2023-08-10","arxiv_id":"2308.05379","repositories_listed":1,"syntology":null},{"url":"/paper/deep-fusion-transformer-network-with-weighted","slug":"deep-fusion-transformer-network-with-weighted","title":"Deep Fusion Transformer Network with Weighted Vector-Wise Keypoints Voting for Robust 6D Object Pose Estimation","date":"2023-08-10","arxiv_id":"2308.05438","repositories_listed":1,"syntology":null},{"url":"/paper/vacancysbert-the-approach-for-representation","slug":"vacancysbert-the-approach-for-representation","title":"VacancySBERT: the approach for representation of titles and skills for semantic similarity search in the recruitment domain","date":"2023-07-31","arxiv_id":"2307.16638","repositories_listed":1,"syntology":null},{"url":"/paper/longitudinal-data-and-a-semantic-similarity","slug":"longitudinal-data-and-a-semantic-similarity","title":"Longitudinal Data and a Semantic Similarity Reward for Chest X-Ray Report Generation","date":"2023-07-19","arxiv_id":"2307.09758","repositories_listed":1,"syntology":null},{"url":"/paper/distilling-coarse-to-fine-semantic-matching","slug":"distilling-coarse-to-fine-semantic-matching","title":"Distilling Coarse-to-Fine Semantic Matching Knowledge for Weakly Supervised 3D Visual Grounding","date":"2023-07-18","arxiv_id":"2307.09267","repositories_listed":1,"syntology":null},{"url":"/paper/large-scale-evaluation-of-topic-models-and","slug":"large-scale-evaluation-of-topic-models-and","title":"Large-Scale Evaluation of Topic Models and Dimensionality Reduction Methods for 2D Text Spatialization","date":"2023-07-17","arxiv_id":"2307.11770","repositories_listed":1,"syntology":null},{"url":"/paper/histopathology-whole-slide-image-analysis-1","slug":"histopathology-whole-slide-image-analysis-1","title":"Histopathology Whole Slide Image Analysis with Heterogeneous Graph Representation Learning","date":"2023-07-09","arxiv_id":"2307.04189","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":6,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/histopathology-whole-slide-image-analysis-1#ran","syntology_url":"https://syntology.ai/paper/2307.04189","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.04189"}},"official":{"repos":["hku-medai/wsi-hgnn"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/novel-categories-discovery-from-probability","slug":"novel-categories-discovery-from-probability","title":"Novel Categories Discovery Via Constraints on Empirical Prediction Statistics","date":"2023-07-07","arxiv_id":"2307.03856","repositories_listed":1,"syntology":null},{"url":"/paper/automatic-design-of-semantic-similarity","slug":"automatic-design-of-semantic-similarity","title":"Automatic Design of Semantic Similarity Ensembles Using Grammatical Evolution","date":"2023-07-03","arxiv_id":"2307.00925","repositories_listed":1,"syntology":null},{"url":"/paper/transfer-learning-for-semantic-similarity","slug":"transfer-learning-for-semantic-similarity","title":"Transfer learning for semantic similarity measures based on symbolic regression","date":"2023-07-02","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/full-automation-of-goal-driven-llm-dialog","slug":"full-automation-of-goal-driven-llm-dialog","title":"Full Automation of Goal-driven LLM Dialog Threads with And-Or Recursors and Refiner Oracles","date":"2023-06-24","arxiv_id":"2306.14077","repositories_listed":1,"syntology":null},{"url":"/paper/sefnet-bridging-tabular-datasets-with","slug":"sefnet-bridging-tabular-datasets-with","title":"SeFNet: Bridging Tabular Datasets with Semantic Feature Nets","date":"2023-06-20","arxiv_id":"2306.11636","repositories_listed":1,"syntology":null},{"url":"/paper/unbalanced-optimal-transport-for-unbalanced","slug":"unbalanced-optimal-transport-for-unbalanced","title":"Unbalanced Optimal Transport for Unbalanced Word Alignment","date":"2023-06-07","arxiv_id":"2306.04116","repositories_listed":1,"syntology":{"n":14,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/unbalanced-optimal-transport-for-unbalanced#ran","syntology_url":"https://syntology.ai/paper/2306.04116","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.04116"}},"official":{"repos":["yukiar/otalign"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/augmenting-reddit-posts-to-determine-wellness","slug":"augmenting-reddit-posts-to-determine-wellness","title":"Augmenting Reddit Posts to Determine Wellness Dimensions impacting Mental Health","date":"2023-06-06","arxiv_id":"2306.04059","repositories_listed":1,"syntology":null},{"url":"/paper/supervised-knowledge-may-hurt-novel-class","slug":"supervised-knowledge-may-hurt-novel-class","title":"Supervised Knowledge May Hurt Novel Class Discovery Performance","date":"2023-06-06","arxiv_id":"2306.03648","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/supervised-knowledge-may-hurt-novel-class#ran","syntology_url":"https://syntology.ai/paper/2306.03648","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.03648"}},"official":{"repos":["j-l-o/sk-hurt-ncd"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/lyricsim-a-novel-dataset-and-benchmark-for","slug":"lyricsim-a-novel-dataset-and-benchmark-for","title":"LyricSIM: A novel Dataset and Benchmark for Similarity Detection in Spanish Song LyricS","date":"2023-06-02","arxiv_id":"2306.01325","repositories_listed":1,"syntology":null},{"url":"/paper/estimating-semantic-similarity-between-in","slug":"estimating-semantic-similarity-between-in","title":"Estimating Semantic Similarity between In-Domain and Out-of-Domain Samples","date":"2023-06-01","arxiv_id":"2306.01206","repositories_listed":1,"syntology":null},{"url":"/paper/exploring-anisotropy-and-outliers-in","slug":"exploring-anisotropy-and-outliers-in","title":"Exploring Anisotropy and Outliers in Multilingual Language Models for Cross-Lingual Semantic Sentence Similarity","date":"2023-06-01","arxiv_id":"2306.00458","repositories_listed":1,"syntology":null},{"url":"/paper/vocabulary-free-image-classification-1","slug":"vocabulary-free-image-classification-1","title":"Vocabulary-free Image Classification","date":"2023-06-01","arxiv_id":"2306.00917","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/vocabulary-free-image-classification-1#ran","syntology_url":"https://syntology.ai/paper/2306.00917","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.00917"}},"official":{"repos":["altndrr/vic"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/datasets-for-portuguese-legal-semantic","slug":"datasets-for-portuguese-legal-semantic","title":"Datasets for Portuguese Legal Semantic Textual Similarity: Comparing weak supervision and an annotation process approaches","date":"2023-05-29","arxiv_id":"2306.00007","repositories_listed":1,"syntology":null},{"url":"/paper/modeling-adversarial-attack-on-pre-trained","slug":"modeling-adversarial-attack-on-pre-trained","title":"Modeling Adversarial Attack on Pre-trained Language Models as Sequential Decision Making","date":"2023-05-27","arxiv_id":"2305.17440","repositories_listed":1,"syntology":null},{"url":"/paper/evaluating-open-domain-dialogues-in-latent","slug":"evaluating-open-domain-dialogues-in-latent","title":"Evaluating Open-Domain Dialogues in Latent Space with Next Sentence Prediction and Mutual Information","date":"2023-05-26","arxiv_id":"2305.16967","repositories_listed":1,"syntology":null},{"url":"/paper/paraamr-a-large-scale-syntactically-diverse","slug":"paraamr-a-large-scale-syntactically-diverse","title":"ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translation","date":"2023-05-26","arxiv_id":"2305.16585","repositories_listed":1,"syntology":null},{"url":"/paper/bridging-continuous-and-discrete-spaces","slug":"bridging-continuous-and-discrete-spaces","title":"Bridging Continuous and Discrete Spaces: Interpretable Sentence Representation Learning via Compositional Operations","date":"2023-05-24","arxiv_id":"2305.14599","repositories_listed":1,"syntology":{"n":12,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":12,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/bridging-continuous-and-discrete-spaces#ran","syntology_url":"https://syntology.ai/paper/2305.14599","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.14599"}},"official":{"repos":["jyhuang36/intersent"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/csts-conditional-semantic-textual-similarity","slug":"csts-conditional-semantic-textual-similarity","title":"C-STS: Conditional Semantic Textual Similarity","date":"2023-05-24","arxiv_id":"2305.15093","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":4,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/csts-conditional-semantic-textual-similarity#ran","syntology_url":"https://syntology.ai/paper/2305.15093","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.15093"}},"official":{"repos":["princeton-nlp/c-sts"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/samscore-a-semantic-structural-similarity","slug":"samscore-a-semantic-structural-similarity","title":"SAMScore: A Content Structural Similarity Metric for Image Translation Evaluation","date":"2023-05-24","arxiv_id":"2305.15367","repositories_listed":1,"syntology":null},{"url":"/paper/sneakyprompt-evaluating-robustness-of-text-to","slug":"sneakyprompt-evaluating-robustness-of-text-to","title":"SneakyPrompt: Jailbreaking Text-to-image Generative Models","date":"2023-05-20","arxiv_id":"2305.12082","repositories_listed":1,"syntology":null},{"url":"/paper/interpretable-word-sense-representations-via","slug":"interpretable-word-sense-representations-via","title":"Interpretable Word Sense Representations via Definition Generation: The Case of Semantic Change Analysis","date":"2023-05-19","arxiv_id":"2305.11993","repositories_listed":1,"syntology":null},{"url":"/paper/balancing-lexical-and-semantic-quality-in","slug":"balancing-lexical-and-semantic-quality-in","title":"Balancing Lexical and Semantic Quality in Abstractive Summarization","date":"2023-05-17","arxiv_id":"2305.09898","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/balancing-lexical-and-semantic-quality-in#ran","syntology_url":"https://syntology.ai/paper/2305.09898","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.09898"}},"official":{"repos":["jeewoo1025/balsum"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/similarity-weighted-construction-of","slug":"similarity-weighted-construction-of","title":"Similarity-weighted Construction of Contextualized Commonsense Knowledge Graphs for Knowledge-intense Argumentation Tasks","date":"2023-05-15","arxiv_id":"2305.08495","repositories_listed":1,"syntology":null},{"url":"/paper/instance-smoothed-contrastive-learning-for","slug":"instance-smoothed-contrastive-learning-for","title":"Instance Smoothed Contrastive Learning for Unsupervised Sentence Embedding","date":"2023-05-12","arxiv_id":"2305.07424","repositories_listed":1,"syntology":null},{"url":"/paper/smatch-standardized-and-extended-evaluation","slug":"smatch-standardized-and-extended-evaluation","title":"SMATCH++: Standardized and Extended Evaluation of Semantic Graphs","date":"2023-05-11","arxiv_id":"2305.06993","repositories_listed":1,"syntology":null},{"url":"/paper/large-language-models-in-biomedical-natural","slug":"large-language-models-in-biomedical-natural","title":"Benchmarking large language models for biomedical natural language processing applications and recommendations","date":"2023-05-10","arxiv_id":"2305.16326","repositories_listed":1,"syntology":null},{"url":"/paper/context-aware-semantic-similarity-measurement","slug":"context-aware-semantic-similarity-measurement","title":"Context-Aware Semantic Similarity Measurement for Unsupervised Word Sense Disambiguation","date":"2023-05-05","arxiv_id":"2305.03520","repositories_listed":1,"syntology":null},{"url":"/paper/on-contrastive-learning-of-semantic","slug":"on-contrastive-learning-of-semantic","title":"REINFOREST: Reinforcing Semantic Code Similarity for Cross-Lingual Code Search Models","date":"2023-05-05","arxiv_id":"2305.03843","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-dialogue-topic-segmentation-with","slug":"unsupervised-dialogue-topic-segmentation-with","title":"Unsupervised Dialogue Topic Segmentation with Topic-aware Utterance Representation","date":"2023-05-04","arxiv_id":"2305.02747","repositories_listed":1,"syntology":null},{"url":"/paper/low-resource-bilingual-dialect-lexicon","slug":"low-resource-bilingual-dialect-lexicon","title":"Low-resource Bilingual Dialect Lexicon Induction with Large Language Models","date":"2023-04-19","arxiv_id":"2304.09957","repositories_listed":1,"syntology":null},{"url":"/paper/pcpnet-an-efficient-and-semantic-enhanced","slug":"pcpnet-an-efficient-and-semantic-enhanced","title":"PCPNet: An Efficient and Semantic-Enhanced Transformer Network for Point Cloud Prediction","date":"2023-04-16","arxiv_id":"2304.07773","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/pcpnet-an-efficient-and-semantic-enhanced#ran","syntology_url":"https://syntology.ai/paper/2304.07773","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.07773"}},"official":{"repos":["blurryface0814/pcpnet"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/usnid-a-framework-for-unsupervised-and-semi","slug":"usnid-a-framework-for-unsupervised-and-semi","title":"A Clustering Framework for Unsupervised and Semi-supervised New Intent Discovery","date":"2023-04-16","arxiv_id":"2304.07699","repositories_listed":1,"syntology":null},{"url":"/paper/a-novel-patent-similarity-measurement","slug":"a-novel-patent-similarity-measurement","title":"A Novel Patent Similarity Measurement Methodology: Semantic Distance and Technological Distance","date":"2023-03-23","arxiv_id":"2303.16767","repositories_listed":1,"syntology":null},{"url":"/paper/micro-video-tagging-via-jointly-modeling","slug":"micro-video-tagging-via-jointly-modeling","title":"Micro-video Tagging via Jointly Modeling Social Influence and Tag Relation","date":"2023-03-15","arxiv_id":"2303.08318","repositories_listed":1,"syntology":null},{"url":"/paper/ino-at-factify-2-structure-coherence-based","slug":"ino-at-factify-2-structure-coherence-based","title":"INO at Factify 2: Structure Coherence based Multi-Modal Fact Verification","date":"2023-03-02","arxiv_id":"2303.01510","repositories_listed":1,"syntology":null},{"url":"/paper/napss-paragraph-level-medical-text","slug":"napss-paragraph-level-medical-text","title":"NapSS: Paragraph-level Medical Text Simplification via Narrative Prompting and Sentence-matching Summarization","date":"2023-02-11","arxiv_id":"2302.05574","repositories_listed":1,"syntology":{"n":24,"n_ran":17,"n_constructed":0,"n_ran_checked":14,"n_instrument":3,"n_unverified":7,"n_honours":1,"n_violates":0,"n_no_contract":13,"n_pointer_only":5,"phrase":"17 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 1 honoured, 0 violated, 13 with no contract checked; 3 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/napss-paragraph-level-medical-text#ran","syntology_url":"https://syntology.ai/paper/2302.05574","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.05574"}},"official":{"repos":["lujunru/napss"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":4,"ran_from_kinds":["found_in_text","official"]}}},{"url":"/paper/transfool-an-adversarial-attack-against","slug":"transfool-an-adversarial-attack-against","title":"TransFool: An Adversarial Attack against Neural Machine Translation Models","date":"2023-02-02","arxiv_id":"2302.00944","repositories_listed":1,"syntology":null},{"url":"/paper/syntactically-robust-training-on-partially","slug":"syntactically-robust-training-on-partially","title":"Syntactically Robust Training on Partially-Observed Data for Open Information Extraction","date":"2023-01-17","arxiv_id":"2301.06841","repositories_listed":1,"syntology":null},{"url":"/paper/user-unified-semantic-enhancement-with","slug":"user-unified-semantic-enhancement-with","title":"USER: Unified Semantic Enhancement with Momentum Contrast for Image-Text Retrieval","date":"2023-01-17","arxiv_id":"2301.06844","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-image-to-point-distillation","slug":"self-supervised-image-to-point-distillation","title":"Self-Supervised Image-to-Point Distillation via Semantically Tolerant Contrastive Loss","date":"2023-01-12","arxiv_id":"2301.05709","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/self-supervised-image-to-point-distillation#ran","syntology_url":"https://syntology.ai/paper/2301.05709","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.05709"}},"official":{"repos":["TRAILab/ST-SLidR"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/the-undesirable-dependence-on-frequency-of","slug":"the-undesirable-dependence-on-frequency-of","title":"The Undesirable Dependence on Frequency of Gender Bias Metrics Based on Word Embeddings","date":"2023-01-02","arxiv_id":"2301.00792","repositories_listed":1,"syntology":null},{"url":"/paper/dip-dual-incongruity-perceiving-network-for","slug":"dip-dual-incongruity-perceiving-network-for","title":"DIP: Dual Incongruity Perceiving Network for Sarcasm Detection","date":"2023-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/efficient-mask-correction-for-click-based","slug":"efficient-mask-correction-for-click-based","title":"Efficient Mask Correction for Click-Based Interactive Image Segmentation","date":"2023-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/beyond-contrastive-learning-a-variational","slug":"beyond-contrastive-learning-a-variational","title":"Beyond Contrastive Learning: A Variational Generative Model for Multilingual Retrieval","date":"2022-12-21","arxiv_id":"2212.10726","repositories_listed":1,"syntology":null},{"url":"/paper/graph-based-semantical-extractive-text","slug":"graph-based-semantical-extractive-text","title":"Graph-based Semantical Extractive Text Analysis","date":"2022-12-19","arxiv_id":"2212.09701","repositories_listed":1,"syntology":null},{"url":"/paper/soft-alignment-objectives-for-robust","slug":"soft-alignment-objectives-for-robust","title":"Soft Alignment Objectives for Robust Adaptation of Language Generation","date":"2022-11-29","arxiv_id":"2211.16550","repositories_listed":1,"syntology":null},{"url":"/paper/the-dependence-on-frequency-of-word-embedding","slug":"the-dependence-on-frequency-of-word-embedding","title":"Investigating the Frequency Distortion of Word Embeddings and Its Impact on Bias Metrics","date":"2022-11-15","arxiv_id":"2211.08203","repositories_listed":1,"syntology":null},{"url":"/paper/improving-word-mover-s-distance-by-leveraging","slug":"improving-word-mover-s-distance-by-leveraging","title":"Improving word mover's distance by leveraging self-attention matrix","date":"2022-11-11","arxiv_id":"2211.06229","repositories_listed":1,"syntology":null},{"url":"/paper/learning-semantic-textual-similarity-via","slug":"learning-semantic-textual-similarity-via","title":"Learning Semantic Textual Similarity via Topic-informed Discrete Latent Variables","date":"2022-11-07","arxiv_id":"2211.03616","repositories_listed":1,"syntology":null},{"url":"/paper/rquge-reference-free-metric-for-evaluating","slug":"rquge-reference-free-metric-for-evaluating","title":"RQUGE: Reference-Free Metric for Evaluating Question Generation by Answering the Question","date":"2022-11-02","arxiv_id":"2211.01482","repositories_listed":1,"syntology":null},{"url":"/paper/you-can-t-pick-your-neighbors-or-can-you-when","slug":"you-can-t-pick-your-neighbors-or-can-you-when","title":"You can't pick your neighbors, or can you? When and how to rely on retrieval in the $k$NN-LM","date":"2022-10-28","arxiv_id":"2210.15859","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/you-can-t-pick-your-neighbors-or-can-you-when#ran","syntology_url":"https://syntology.ai/paper/2210.15859","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.15859"}},"official":{"repos":["iesl/knnlm-retrieval-quality"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/towards-better-text-image-consistency-in-text","slug":"towards-better-text-image-consistency-in-text","title":"SSD: Towards Better Text-Image Consistency Metric in Text-to-Image Generation","date":"2022-10-27","arxiv_id":"2210.15235","repositories_listed":1,"syntology":null},{"url":"/paper/improving-adversarial-robustness-with-self","slug":"improving-adversarial-robustness-with-self","title":"Improving Adversarial Robustness with Self-Paced Hard-Class Pair Reweighting","date":"2022-10-26","arxiv_id":"2210.15068","repositories_listed":1,"syntology":null},{"url":"/paper/pointly-supervised-panoptic-segmentation","slug":"pointly-supervised-panoptic-segmentation","title":"Pointly-Supervised Panoptic Segmentation","date":"2022-10-25","arxiv_id":"2210.13950","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/pointly-supervised-panoptic-segmentation#ran","syntology_url":"https://syntology.ai/paper/2210.13950","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.13950"}},"official":{"repos":["bravegroup/psps"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/describing-sets-of-images-with-textual-pca","slug":"describing-sets-of-images-with-textual-pca","title":"Describing Sets of Images with Textual-PCA","date":"2022-10-21","arxiv_id":"2210.12112","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/describing-sets-of-images-with-textual-pca#ran","syntology_url":"https://syntology.ai/paper/2210.12112","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.12112"}},"official":{"repos":["odedh/textual-pca"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/retrofitting-multilingual-sentence-embeddings","slug":"retrofitting-multilingual-sentence-embeddings","title":"Retrofitting Multilingual Sentence Embeddings with Abstract Meaning Representation","date":"2022-10-18","arxiv_id":"2210.09773","repositories_listed":1,"syntology":null},{"url":"/paper/gma3d-local-global-attention-learning-to","slug":"gma3d-local-global-attention-learning-to","title":"GMA3D: Local-Global Attention Learning to Estimate Occluded Motions of Scene Flow","date":"2022-10-07","arxiv_id":"2210.03296","repositories_listed":1,"syntology":null},{"url":"/paper/weak-shot-semantic-segmentation-via-dual","slug":"weak-shot-semantic-segmentation-via-dual","title":"Weak-shot Semantic Segmentation via Dual Similarity Transfer","date":"2022-10-05","arxiv_id":"2210.02270","repositories_listed":1,"syntology":null},{"url":"/paper/a-generalized-method-for-automated","slug":"a-generalized-method-for-automated","title":"A Generalized Method for Automated Multilingual Loanword Detection","date":"2022-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null}],"record_sha256":"722034569a1f2475847c9eb90e1f28aa9b84da7a02b3e6b4aa87c9b34e5c0e17","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}