{"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-textual-similarity/papers/2","list_of":"/task/semantic-textual-similarity","task":"Semantic Textual 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":2,"pages_in_order":24,"rows_per_page":100,"rows":[101,200],"of":2381,"counts":{"archive_papers_tagged":2381,"with_a_code_link":693,"where_syntology_ran_a_sample":144,"not_listed_spam_title":0,"listed":2381,"listed_where_code_ran":144,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":118,"every_run_a_failure_of_syntologys_instrument":26,"listed_with_a_run_with_no_instrument_failure":118,"listed_every_run_a_failure_of_syntologys_instrument":26,"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-textual-similarity","prev":"/task/semantic-textual-similarity","next":"/task/semantic-textual-similarity/papers/3","papers":[{"url":"/paper/glyce-glyph-vectors-for-chinese-character","slug":"glyce-glyph-vectors-for-chinese-character","title":"Glyce: Glyph-vectors for Chinese Character Representations","date":"2019-01-29","arxiv_id":"1901.10125","repositories_listed":2,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":5,"n_pointer_only":1,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 2 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/glyce-glyph-vectors-for-chinese-character#ran","syntology_url":"https://syntology.ai/paper/1901.10125","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.10125"}},"official":{"repos":["ShannonAI/glyce"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/auto-encoding-dictionary-definitions-into","slug":"auto-encoding-dictionary-definitions-into","title":"Auto-Encoding Dictionary Definitions into Consistent Word Embeddings","date":"2018-10-01","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/generating-more-interesting-responses-in","slug":"generating-more-interesting-responses-in","title":"Generating More Interesting Responses in Neural Conversation Models with Distributional Constraints","date":"2018-09-04","arxiv_id":"1809.01215","repositories_listed":2,"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/generating-more-interesting-responses-in#ran","syntology_url":"https://syntology.ai/paper/1809.01215","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.01215"}},"official":{"repos":["abaheti95/DC-NeuralConversation"],"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/cross-lingual-cross-platform-rumor","slug":"cross-lingual-cross-platform-rumor","title":"Cross-Lingual Cross-Platform Rumor Verification Pivoting on Multimedia Content","date":"2018-08-14","arxiv_id":"1808.04911","repositories_listed":2,"syntology":null},{"url":"/paper/a-joint-sequence-fusion-model-for-video","slug":"a-joint-sequence-fusion-model-for-video","title":"A Joint Sequence Fusion Model for Video Question Answering and Retrieval","date":"2018-08-07","arxiv_id":"1808.02559","repositories_listed":2,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/a-joint-sequence-fusion-model-for-video#ran","syntology_url":"https://syntology.ai/paper/1808.02559","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1808.02559"}},"official":null}},{"url":"/paper/large-scale-multi-domain-belief-tracking-with","slug":"large-scale-multi-domain-belief-tracking-with","title":"Large-Scale Multi-Domain Belief Tracking with Knowledge Sharing","date":"2018-07-17","arxiv_id":"1807.06517","repositories_listed":2,"syntology":null},{"url":"/paper/ad-hoc-table-retrieval-using-semantic","slug":"ad-hoc-table-retrieval-using-semantic","title":"Ad Hoc Table Retrieval using Semantic Similarity","date":"2018-02-16","arxiv_id":"1802.06159","repositories_listed":2,"syntology":null},{"url":"/paper/a-semantics-based-measure-of-emoji-similarity","slug":"a-semantics-based-measure-of-emoji-similarity","title":"A Semantics-Based Measure of Emoji Similarity","date":"2017-07-14","arxiv_id":"1707.04653","repositories_listed":2,"syntology":null},{"url":"/paper/semantic-specialisation-of-distributional","slug":"semantic-specialisation-of-distributional","title":"Semantic Specialisation of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints","date":"2017-06-01","arxiv_id":"1706.00374","repositories_listed":2,"syntology":null},{"url":"/paper/no-fuss-distance-metric-learning-using","slug":"no-fuss-distance-metric-learning-using","title":"No Fuss Distance Metric Learning using Proxies","date":"2017-03-21","arxiv_id":"1703.07464","repositories_listed":2,"syntology":null},{"url":"/paper/counter-fitting-word-vectors-to-linguistic","slug":"counter-fitting-word-vectors-to-linguistic","title":"Counter-fitting Word Vectors to Linguistic Constraints","date":"2016-03-02","arxiv_id":"1603.00892","repositories_listed":2,"syntology":null},{"url":"/paper/using-information-content-to-evaluate","slug":"using-information-content-to-evaluate","title":"Using Information Content to Evaluate Semantic Similarity in a Taxonomy","date":"1995-11-29","arxiv_id":"cmp-lg/9511007","repositories_listed":2,"syntology":null},{"url":"/paper/fa-forced-prompt-learning-of-vision-language","slug":"fa-forced-prompt-learning-of-vision-language","title":"FA: Forced Prompt Learning of Vision-Language Models for Out-of-Distribution Detection","date":"2025-07-06","arxiv_id":"2507.04511","repositories_listed":1,"syntology":{"n":9,"n_ran":5,"n_constructed":0,"n_ran_checked":2,"n_instrument":3,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":9,"phrase":"5 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; 3 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/fa-forced-prompt-learning-of-vision-language#ran","syntology_url":"https://syntology.ai/paper/2507.04511","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2507.04511"}},"official":{"repos":["0xfafa/fa"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/impliret-benchmarking-the-implicit-fact","slug":"impliret-benchmarking-the-implicit-fact","title":"ImpliRet: Benchmarking the Implicit Fact Retrieval Challenge","date":"2025-06-17","arxiv_id":"2506.14407","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":2,"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/impliret-benchmarking-the-implicit-fact#ran","syntology_url":"https://syntology.ai/paper/2506.14407","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.14407"}},"official":{"repos":["zeinabtaghavi/impliret"],"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/inv-entropy-a-fully-probabilistic-framework","slug":"inv-entropy-a-fully-probabilistic-framework","title":"Inv-Entropy: A Fully Probabilistic Framework for Uncertainty Quantification in Language Models","date":"2025-06-11","arxiv_id":"2506.09684","repositories_listed":1,"syntology":null},{"url":"/paper/trend-aware-fashion-recommendation-with","slug":"trend-aware-fashion-recommendation-with","title":"Trend-Aware Fashion Recommendation with Visual Segmentation and Semantic Similarity","date":"2025-06-09","arxiv_id":"2506.07773","repositories_listed":1,"syntology":null},{"url":"/paper/knn-defense-defense-against-3d-adversarial","slug":"knn-defense-defense-against-3d-adversarial","title":"KNN-Defense: Defense against 3D Adversarial Point Clouds using Nearest-Neighbor Search","date":"2025-06-07","arxiv_id":"2506.06906","repositories_listed":1,"syntology":null},{"url":"/paper/irt-router-effective-and-interpretable-multi","slug":"irt-router-effective-and-interpretable-multi","title":"IRT-Router: Effective and Interpretable Multi-LLM Routing via Item Response Theory","date":"2025-06-01","arxiv_id":"2506.01048","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":2,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":2,"n_pointer_only":3,"phrase":"3 ran (of which 2 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 1 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/irt-router-effective-and-interpretable-multi#ran","syntology_url":"https://syntology.ai/paper/2506.01048","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.01048"}},"official":{"repos":["Mercidaiha/IRT-Router"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":2,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/category-aware-eeg-image-generation-based-on","slug":"category-aware-eeg-image-generation-based-on","title":"Category-aware EEG image generation based on wavelet transform and contrast semantic loss","date":"2025-05-30","arxiv_id":"2505.24301","repositories_listed":1,"syntology":null},{"url":"/paper/prism-a-framework-for-producing-interpretable","slug":"prism-a-framework-for-producing-interpretable","title":"PRISM: A Framework for Producing Interpretable Political Bias Embeddings with Political-Aware Cross-Encoder","date":"2025-05-30","arxiv_id":"2505.24646","repositories_listed":1,"syntology":null},{"url":"/paper/label-guided-in-context-learning-for-named","slug":"label-guided-in-context-learning-for-named","title":"Label-Guided In-Context Learning for Named Entity Recognition","date":"2025-05-29","arxiv_id":"2505.23722","repositories_listed":1,"syntology":null},{"url":"/paper/position-mechanistic-interpretability-should","slug":"position-mechanistic-interpretability-should","title":"Position: Mechanistic Interpretability Should Prioritize Feature Consistency in SAEs","date":"2025-05-26","arxiv_id":"2505.20254","repositories_listed":1,"syntology":null},{"url":"/paper/the-avengers-a-simple-recipe-for-uniting","slug":"the-avengers-a-simple-recipe-for-uniting","title":"The Avengers: A Simple Recipe for Uniting Smaller Language Models to Challenge Proprietary Giants","date":"2025-05-26","arxiv_id":"2505.19797","repositories_listed":1,"syntology":null},{"url":"/paper/hypercube-rag-hypercube-based-retrieval","slug":"hypercube-rag-hypercube-based-retrieval","title":"Hypercube-RAG: Hypercube-Based Retrieval-Augmented Generation for In-domain Scientific Question-Answering","date":"2025-05-25","arxiv_id":"2505.19288","repositories_listed":1,"syntology":null},{"url":"/paper/smoothie-smoothing-diffusion-on-token","slug":"smoothie-smoothing-diffusion-on-token","title":"Smoothie: Smoothing Diffusion on Token Embeddings for Text Generation","date":"2025-05-24","arxiv_id":"2505.18853","repositories_listed":1,"syntology":{"n":12,"n_ran":6,"n_constructed":2,"n_ran_checked":4,"n_instrument":2,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":12,"phrase":"6 ran (of which 2 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/smoothie-smoothing-diffusion-on-token#ran","syntology_url":"https://syntology.ai/paper/2505.18853","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.18853"}},"official":{"repos":["ashaba1in/smoothie"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":2,"n_ran_no_instrument_failure":4,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/accidental-misalignment-fine-tuning-language","slug":"accidental-misalignment-fine-tuning-language","title":"Accidental Misalignment: Fine-Tuning Language Models Induces Unexpected Vulnerability","date":"2025-05-22","arxiv_id":"2505.16789","repositories_listed":1,"syntology":null},{"url":"/paper/equivpruner-boosting-efficiency-and-quality-1","slug":"equivpruner-boosting-efficiency-and-quality-1","title":"EquivPruner: Boosting Efficiency and Quality in LLM-Based Search via Action Pruning","date":"2025-05-22","arxiv_id":"2505.16312","repositories_listed":1,"syntology":null},{"url":"/paper/llms-are-not-scorers-rethinking-mt-evaluation","slug":"llms-are-not-scorers-rethinking-mt-evaluation","title":"LLMs Are Not Scorers: Rethinking MT Evaluation with Generation-Based Methods","date":"2025-05-22","arxiv_id":"2505.16129","repositories_listed":1,"syntology":null},{"url":"/paper/instructsam-a-training-free-framework-for","slug":"instructsam-a-training-free-framework-for","title":"InstructSAM: A Training-Free Framework for Instruction-Oriented Remote Sensing Object Recognition","date":"2025-05-21","arxiv_id":"2505.15818","repositories_listed":1,"syntology":null},{"url":"/paper/leveraging-the-powerful-attention-of-a-pre","slug":"leveraging-the-powerful-attention-of-a-pre","title":"Leveraging the Powerful Attention of a Pre-trained Diffusion Model for Exemplar-based Image Colorization","date":"2025-05-21","arxiv_id":"2505.15812","repositories_listed":1,"syntology":null},{"url":"/paper/multihal-multilingual-dataset-for-knowledge","slug":"multihal-multilingual-dataset-for-knowledge","title":"MultiHal: Multilingual Dataset for Knowledge-Graph Grounded Evaluation of LLM Hallucinations","date":"2025-05-20","arxiv_id":"2505.14101","repositories_listed":1,"syntology":null},{"url":"/paper/r2med-a-benchmark-for-reasoning-driven","slug":"r2med-a-benchmark-for-reasoning-driven","title":"R2MED: A Benchmark for Reasoning-Driven Medical Retrieval","date":"2025-05-20","arxiv_id":"2505.14558","repositories_listed":1,"syntology":null},{"url":"/paper/contrastive-prompting-enhances-sentence","slug":"contrastive-prompting-enhances-sentence","title":"Contrastive Prompting Enhances Sentence Embeddings in LLMs through Inference-Time Steering","date":"2025-05-19","arxiv_id":"2505.12831","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-heuristics-generation-for-solving","slug":"efficient-heuristics-generation-for-solving","title":"Efficient Heuristics Generation for Solving Combinatorial Optimization Problems Using Large Language Models","date":"2025-05-19","arxiv_id":"2505.12627","repositories_listed":1,"syntology":null},{"url":"/paper/one-step-offline-distillation-of-diffusion","slug":"one-step-offline-distillation-of-diffusion","title":"One-Step Offline Distillation of Diffusion-based Models via Koopman Modeling","date":"2025-05-19","arxiv_id":"2505.13358","repositories_listed":1,"syntology":null},{"url":"/paper/elite-embedding-less-retrieval-with-iterative","slug":"elite-embedding-less-retrieval-with-iterative","title":"ELITE: Embedding-Less retrieval with Iterative Text Exploration","date":"2025-05-17","arxiv_id":"2505.11908","repositories_listed":1,"syntology":null},{"url":"/paper/2505-11326","slug":"2505-11326","title":"Temporally-Grounded Language Generation: A Benchmark for Real-Time Vision-Language Models","date":"2025-05-16","arxiv_id":"2505.11326","repositories_listed":1,"syntology":null},{"url":"/paper/ldir-low-dimensional-dense-and-interpretable","slug":"ldir-low-dimensional-dense-and-interpretable","title":"LDIR: Low-Dimensional Dense and Interpretable Text Embeddings with Relative Representations","date":"2025-05-15","arxiv_id":"2505.10354","repositories_listed":1,"syntology":null},{"url":"/paper/are-llms-complicated-ethical-dilemma","slug":"are-llms-complicated-ethical-dilemma","title":"Are LLMs complicated ethical dilemma analyzers?","date":"2025-05-12","arxiv_id":"2505.08106","repositories_listed":1,"syntology":null},{"url":"/paper/concept-level-explainability-for-auditing","slug":"concept-level-explainability-for-auditing","title":"Concept-Level Explainability for Auditing & Steering LLM Responses","date":"2025-05-12","arxiv_id":"2505.07610","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/concept-level-explainability-for-auditing#ran","syntology_url":"https://syntology.ai/paper/2505.07610","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.07610"}},"official":{"repos":["k-amara/ConceptX"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/20min-xd-a-comparable-corpus-of-swiss-news","slug":"20min-xd-a-comparable-corpus-of-swiss-news","title":"20min-XD: A Comparable Corpus of Swiss News Articles","date":"2025-04-30","arxiv_id":"2504.21677","repositories_listed":1,"syntology":null},{"url":"/paper/towards-a-multi-agent-vision-language-system","slug":"towards-a-multi-agent-vision-language-system","title":"Towards a Multi-Agent Vision-Language System for Zero-Shot Novel Hazardous Object Detection for Autonomous Driving Safety","date":"2025-04-18","arxiv_id":"2504.13399","repositories_listed":1,"syntology":{"n":2,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 2 unverified","sample_list":"/paper/towards-a-multi-agent-vision-language-system#ran","syntology_url":"https://syntology.ai/paper/2504.13399","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2504.13399"}},"official":{"repos":["mi3labucm/coooler"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"url":"/paper/cdf-rag-causal-dynamic-feedback-for-adaptive","slug":"cdf-rag-causal-dynamic-feedback-for-adaptive","title":"CDF-RAG: Causal Dynamic Feedback for Adaptive Retrieval-Augmented Generation","date":"2025-04-17","arxiv_id":"2504.12560","repositories_listed":1,"syntology":null},{"url":"/paper/ontology-based-semantic-similarity-measures","slug":"ontology-based-semantic-similarity-measures","title":"Ontology-based Semantic Similarity Measures for Clustering Medical Concepts in Drug Safety","date":"2025-03-26","arxiv_id":"2503.20737","repositories_listed":1,"syntology":null},{"url":"/paper/high-temporal-consistency-through-semantic","slug":"high-temporal-consistency-through-semantic","title":"High Temporal Consistency through Semantic Similarity Propagation in Semi-Supervised Video Semantic Segmentation for Autonomous Flight","date":"2025-03-19","arxiv_id":"2503.15676","repositories_listed":1,"syntology":{"n":39,"n_ran":25,"n_constructed":15,"n_ran_checked":20,"n_instrument":5,"n_unverified":14,"n_honours":0,"n_violates":0,"n_no_contract":20,"n_pointer_only":39,"phrase":"25 ran (of which 15 constructed an object rather than computing a result; 20 with no instrument failure: 0 honoured, 0 violated, 20 with no contract checked; 5 where Syntology's instrument failed) · 14 unverified","sample_list":"/paper/high-temporal-consistency-through-semantic#ran","syntology_url":"https://syntology.ai/paper/2503.15676","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.15676"}},"official":{"repos":["fraunhoferivi/ssp"],"state":"official (archive's flag): 25 ran","n_ran":25,"n_constructed":15,"n_ran_no_instrument_failure":20,"n_unverified":14,"ran_from_kinds":["official"]}}},{"url":"/paper/tlac-two-stage-lmm-augmented-clip-for-zero","slug":"tlac-two-stage-lmm-augmented-clip-for-zero","title":"TLAC: Two-stage LMM Augmented CLIP for Zero-Shot Classification","date":"2025-03-15","arxiv_id":"2503.12206","repositories_listed":1,"syntology":null},{"url":"/paper/domain-adaptation-for-japanese-sentence","slug":"domain-adaptation-for-japanese-sentence","title":"Domain Adaptation for Japanese Sentence Embeddings with Contrastive Learning based on Synthetic Sentence Generation","date":"2025-03-12","arxiv_id":"2503.09094","repositories_listed":1,"syntology":null},{"url":"/paper/promptmap-an-alternative-interaction-style","slug":"promptmap-an-alternative-interaction-style","title":"PromptMap: An Alternative Interaction Style for AI-Based Image Generation","date":"2025-03-12","arxiv_id":"2503.09436","repositories_listed":1,"syntology":null},{"url":"/paper/asymmetric-visual-semantic-embedding","slug":"asymmetric-visual-semantic-embedding","title":"Asymmetric Visual Semantic Embedding Framework for Efficient Vision-Language Alignment","date":"2025-03-10","arxiv_id":"2503.06974","repositories_listed":1,"syntology":null},{"url":"/paper/sgc-net-stratified-granular-comparison","slug":"sgc-net-stratified-granular-comparison","title":"SGC-Net: Stratified Granular Comparison Network for Open-Vocabulary HOI Detection","date":"2025-03-01","arxiv_id":"2503.00414","repositories_listed":1,"syntology":null},{"url":"/paper/mitigating-hallucinations-in-large-vision-4","slug":"mitigating-hallucinations-in-large-vision-4","title":"Mitigating Hallucinations in Large Vision-Language Models by Adaptively Constraining Information Flow","date":"2025-02-28","arxiv_id":"2502.20750","repositories_listed":1,"syntology":{"n":11,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":4,"n_honours":0,"n_violates":1,"n_no_contract":5,"n_pointer_only":2,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 1 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/mitigating-hallucinations-in-large-vision-4#ran","syntology_url":"https://syntology.ai/paper/2502.20750","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.20750"}},"official":{"repos":["jiaqi5598/adavib"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/climb-3d-continual-learning-for-imbalanced-3d","slug":"climb-3d-continual-learning-for-imbalanced-3d","title":"CLIMB-3D: Continual Learning for Imbalanced 3D Instance Segmentation","date":"2025-02-24","arxiv_id":"2502.17429","repositories_listed":1,"syntology":null},{"url":"/paper/exploring-rwkv-for-sentence-embeddings-layer","slug":"exploring-rwkv-for-sentence-embeddings-layer","title":"Exploring RWKV for Sentence Embeddings: Layer-wise Analysis and Baseline Comparison for Semantic Similarity","date":"2025-02-20","arxiv_id":"2502.14620","repositories_listed":1,"syntology":null},{"url":"/paper/finmteb-finance-massive-text-embedding","slug":"finmteb-finance-massive-text-embedding","title":"FinMTEB: Finance Massive Text Embedding Benchmark","date":"2025-02-16","arxiv_id":"2502.10990","repositories_listed":1,"syntology":null},{"url":"/paper/bridging-llm-generated-code-and-requirements","slug":"bridging-llm-generated-code-and-requirements","title":"Bridging LLM-Generated Code and Requirements: Reverse Generation technique and SBC Metric for Developer Insights","date":"2025-02-11","arxiv_id":"2502.07835","repositories_listed":1,"syntology":null},{"url":"/paper/elevating-legal-llm-responses-harnessing","slug":"elevating-legal-llm-responses-harnessing","title":"Elevating Legal LLM Responses: Harnessing Trainable Logical Structures and Semantic Knowledge with Legal Reasoning","date":"2025-02-11","arxiv_id":"2502.07912","repositories_listed":1,"syntology":null},{"url":"/paper/fake-news-detection-after-llm-laundering","slug":"fake-news-detection-after-llm-laundering","title":"Fake News Detection After LLM Laundering: Measurement and Explanation","date":"2025-01-29","arxiv_id":"2501.18649","repositories_listed":1,"syntology":null},{"url":"/paper/causal-graphs-meet-thoughts-enhancing-complex","slug":"causal-graphs-meet-thoughts-enhancing-complex","title":"Causal Graphs Meet Thoughts: Enhancing Complex Reasoning in Graph-Augmented LLMs","date":"2025-01-24","arxiv_id":"2501.14892","repositories_listed":1,"syntology":null},{"url":"/paper/rethinking-the-sample-relations-for-few-shot","slug":"rethinking-the-sample-relations-for-few-shot","title":"Rethinking the Sample Relations for Few-Shot Classification","date":"2025-01-23","arxiv_id":"2501.13418","repositories_listed":1,"syntology":null},{"url":"/paper/medfilip-medical-fine-grained-language-image","slug":"medfilip-medical-fine-grained-language-image","title":"MedFILIP: Medical Fine-grained Language-Image Pre-training","date":"2025-01-18","arxiv_id":"2501.10775","repositories_listed":1,"syntology":null},{"url":"/paper/anchor-aware-similarity-cohesion-in-target","slug":"anchor-aware-similarity-cohesion-in-target","title":"Anchor-Aware Similarity Cohesion in Target Frames Enables Predicting Temporal Moment Boundaries in 2D","date":"2025-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/harnessing-frozen-unimodal-encoders-for","slug":"harnessing-frozen-unimodal-encoders-for","title":"Harnessing Frozen Unimodal Encoders for Flexible Multimodal Alignment","date":"2025-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/reasoning-to-attend-try-to-understand-how-seg","slug":"reasoning-to-attend-try-to-understand-how-seg","title":"Reasoning to Attend: Try to Understand How <SEG> Token Works","date":"2024-12-23","arxiv_id":"2412.17741","repositories_listed":1,"syntology":null},{"url":"/paper/diffsim-taming-diffusion-models-for","slug":"diffsim-taming-diffusion-models-for","title":"DiffSim: Taming Diffusion Models for Evaluating Visual Similarity","date":"2024-12-19","arxiv_id":"2412.14580","repositories_listed":1,"syntology":null},{"url":"/paper/dusss-dual-semantic-similarity-supervised","slug":"dusss-dual-semantic-similarity-supervised","title":"DuSSS: Dual Semantic Similarity-Supervised Vision-Language Model for Semi-Supervised Medical Image Segmentation","date":"2024-12-17","arxiv_id":"2412.12492","repositories_listed":1,"syntology":null},{"url":"/paper/familiarity-better-evaluation-of-zero-shot","slug":"familiarity-better-evaluation-of-zero-shot","title":"Familiarity: Better Evaluation of Zero-Shot Named Entity Recognition by Quantifying Label Shifts in Synthetic Training Data","date":"2024-12-13","arxiv_id":"2412.10121","repositories_listed":1,"syntology":null},{"url":"/paper/quantifying-positional-biases-in-text","slug":"quantifying-positional-biases-in-text","title":"Quantifying Positional Biases in Text Embedding Models","date":"2024-12-13","arxiv_id":"2412.15241","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":0,"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, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/quantifying-positional-biases-in-text#ran","syntology_url":"https://syntology.ai/paper/2412.15241","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.15241"}},"official":{"repos":["sgoel97/neurips-embedding-positional-bias"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/single-view-graph-contrastive-learning-with","slug":"single-view-graph-contrastive-learning-with","title":"Single-View Graph Contrastive Learning with Soft Neighborhood Awareness","date":"2024-12-12","arxiv_id":"2412.09261","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":1,"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/single-view-graph-contrastive-learning-with#ran","syntology_url":"https://syntology.ai/paper/2412.09261","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.09261"}},"official":{"repos":["sunisfighting/signa"],"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/multilingual-llms-inherently-reward-in","slug":"multilingual-llms-inherently-reward-in","title":"Multilingual LLMs Inherently Reward In-Language Time-Sensitive Semantic Alignment for Low-Resource Languages","date":"2024-12-11","arxiv_id":"2412.08090","repositories_listed":1,"syntology":null},{"url":"/paper/tscheater-generating-high-quality-tibetan","slug":"tscheater-generating-high-quality-tibetan","title":"TSCheater: Generating High-Quality Tibetan Adversarial Texts via Visual Similarity","date":"2024-12-03","arxiv_id":"2412.02371","repositories_listed":1,"syntology":null},{"url":"/paper/vid-morp-video-moment-retrieval-pretraining","slug":"vid-morp-video-moment-retrieval-pretraining","title":"Vid-Morp: Video Moment Retrieval Pretraining from Unlabeled Videos in the Wild","date":"2024-12-01","arxiv_id":"2412.00811","repositories_listed":1,"syntology":null},{"url":"/paper/relcon-relative-contrastive-learning-for-a","slug":"relcon-relative-contrastive-learning-for-a","title":"RelCon: Relative Contrastive Learning for a Motion Foundation Model for Wearable Data","date":"2024-11-27","arxiv_id":"2411.18822","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/relcon-relative-contrastive-learning-for-a#ran","syntology_url":"https://syntology.ai/paper/2411.18822","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.18822"}},"official":{"repos":["maxxu05/relcon"],"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/squeezed-attention-accelerating-long-context","slug":"squeezed-attention-accelerating-long-context","title":"Squeezed Attention: Accelerating Long Context Length LLM Inference","date":"2024-11-14","arxiv_id":"2411.09688","repositories_listed":1,"syntology":{"n":15,"n_ran":12,"n_constructed":0,"n_ran_checked":9,"n_instrument":3,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":8,"n_pointer_only":15,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 0 violated, 8 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/squeezed-attention-accelerating-long-context#ran","syntology_url":"https://syntology.ai/paper/2411.09688","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.09688"}},"official":{"repos":["SqueezeAILab/SqueezedAttention"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/semantic-aware-resource-management-for-c-v2x","slug":"semantic-aware-resource-management-for-c-v2x","title":"Semantic-Aware Resource Management for C-V2X Platooning via Multi-Agent Reinforcement Learning","date":"2024-11-07","arxiv_id":"2411.04672","repositories_listed":1,"syntology":null},{"url":"/paper/continual-audio-visual-sound-separation","slug":"continual-audio-visual-sound-separation","title":"Continual Audio-Visual Sound Separation","date":"2024-11-05","arxiv_id":"2411.02860","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/continual-audio-visual-sound-separation#ran","syntology_url":"https://syntology.ai/paper/2411.02860","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.02860"}},"official":{"repos":["weiguopian/contav-sep_neurips2024"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/hacd-harnessing-attribute-semantics-and","slug":"hacd-harnessing-attribute-semantics-and","title":"HACD: Harnessing Attribute Semantics and Mesoscopic Structure for Community Detection","date":"2024-11-04","arxiv_id":"2411.01947","repositories_listed":1,"syntology":null},{"url":"/paper/cmdcaliper-a-semantic-aware-command-line","slug":"cmdcaliper-a-semantic-aware-command-line","title":"CmdCaliper: A Semantic-Aware Command-Line Embedding Model and Dataset for Security Research","date":"2024-11-02","arxiv_id":"2411.01176","repositories_listed":1,"syntology":null},{"url":"/paper/bis-nl2sql-service-evaluation-benchmark-for","slug":"bis-nl2sql-service-evaluation-benchmark-for","title":"BIS: NL2SQL Service Evaluation Benchmark for Business Intelligence Scenarios","date":"2024-10-30","arxiv_id":"2410.22925","repositories_listed":1,"syntology":null},{"url":"/paper/decoupling-semantic-similarity-from-spatial","slug":"decoupling-semantic-similarity-from-spatial","title":"Decoupling Semantic Similarity from Spatial Alignment for Neural Networks","date":"2024-10-30","arxiv_id":"2410.23107","repositories_listed":1,"syntology":null},{"url":"/paper/emotional-rag-enhancing-role-playing-agents","slug":"emotional-rag-enhancing-role-playing-agents","title":"Emotional RAG: Enhancing Role-Playing Agents through Emotional Retrieval","date":"2024-10-30","arxiv_id":"2410.23041","repositories_listed":1,"syntology":null},{"url":"/paper/toeing-the-party-line-election-manifestos-as","slug":"toeing-the-party-line-election-manifestos-as","title":"Toeing the Party Line: Election Manifestos as a Key to Understand Political Discourse on Twitter","date":"2024-10-21","arxiv_id":"2410.15743","repositories_listed":1,"syntology":null},{"url":"/paper/automatically-interpreting-millions-of","slug":"automatically-interpreting-millions-of","title":"Automatically Interpreting Millions of Features in Large Language Models","date":"2024-10-17","arxiv_id":"2410.13928","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"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) · 2 unverified","sample_list":"/paper/automatically-interpreting-millions-of#ran","syntology_url":"https://syntology.ai/paper/2410.13928","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.13928"}},"official":{"repos":["eleutherai/sae-auto-interp"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/boosting-imperceptibility-of-stable-diffusion","slug":"boosting-imperceptibility-of-stable-diffusion","title":"Boosting Imperceptibility of Stable Diffusion-based Adversarial Examples Generation with Momentum","date":"2024-10-17","arxiv_id":"2410.13122","repositories_listed":1,"syntology":null},{"url":"/paper/llaca-multimodal-large-language-continual","slug":"llaca-multimodal-large-language-continual","title":"Large Continual Instruction Assistant","date":"2024-10-08","arxiv_id":"2410.10868","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":0,"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/llaca-multimodal-large-language-continual#ran","syntology_url":"https://syntology.ai/paper/2410.10868","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.10868"}},"official":{"repos":["jingyangqiao/coin"],"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/from-unimodal-to-multimodal-scaling-up","slug":"from-unimodal-to-multimodal-scaling-up","title":"From Unimodal to Multimodal: Scaling up Projectors to Align Modalities","date":"2024-09-28","arxiv_id":"2409.19425","repositories_listed":1,"syntology":null},{"url":"/paper/mitigating-semantic-leakage-in-cross-lingual","slug":"mitigating-semantic-leakage-in-cross-lingual","title":"Mitigating Semantic Leakage in Cross-lingual Embeddings via Orthogonality Constraint","date":"2024-09-24","arxiv_id":"2409.15664","repositories_listed":1,"syntology":null},{"url":"/paper/reasoning-graph-enhanced-exemplars-retrieval","slug":"reasoning-graph-enhanced-exemplars-retrieval","title":"Reasoning Graph Enhanced Exemplars Retrieval for In-Context Learning","date":"2024-09-17","arxiv_id":"2409.11147","repositories_listed":1,"syntology":null},{"url":"/paper/beeformer-bridging-the-gap-between-semantic","slug":"beeformer-bridging-the-gap-between-semantic","title":"beeFormer: Bridging the Gap Between Semantic and Interaction Similarity in Recommender Systems","date":"2024-09-16","arxiv_id":"2409.10309","repositories_listed":1,"syntology":null},{"url":"/paper/distilling-monolingual-and-crosslingual-word","slug":"distilling-monolingual-and-crosslingual-word","title":"Distilling Monolingual and Crosslingual Word-in-Context Representations","date":"2024-09-13","arxiv_id":"2409.08719","repositories_listed":1,"syntology":null},{"url":"/paper/retro-li-small-scale-retrieval-augmented","slug":"retro-li-small-scale-retrieval-augmented","title":"Retro-li: Small-Scale Retrieval Augmented Generation Supporting Noisy Similarity Searches and Domain Shift Generalization","date":"2024-09-12","arxiv_id":"2410.00004","repositories_listed":1,"syntology":null},{"url":"/paper/subregweigh-effective-and-efficient","slug":"subregweigh-effective-and-efficient","title":"SubRegWeigh: Effective and Efficient Annotation Weighing with Subword Regularization","date":"2024-09-10","arxiv_id":"2409.06216","repositories_listed":1,"syntology":null},{"url":"/paper/self-judge-selective-instruction-following","slug":"self-judge-selective-instruction-following","title":"Self-Judge: Selective Instruction Following with Alignment Self-Evaluation","date":"2024-09-02","arxiv_id":"2409.00935","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"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) · 0 unverified","sample_list":"/paper/self-judge-selective-instruction-following#ran","syntology_url":"https://syntology.ai/paper/2409.00935","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.00935"}},"official":{"repos":["nusnlp/Self-J"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/flowretrieval-flow-guided-data-retrieval-for","slug":"flowretrieval-flow-guided-data-retrieval-for","title":"FlowRetrieval: Flow-Guided Data Retrieval for Few-Shot Imitation Learning","date":"2024-08-29","arxiv_id":"2408.16944","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/flowretrieval-flow-guided-data-retrieval-for#ran","syntology_url":"https://syntology.ai/paper/2408.16944","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.16944"}},"official":null}},{"url":"/paper/concse-unified-contrastive-learning-and","slug":"concse-unified-contrastive-learning-and","title":"ConCSE: Unified Contrastive Learning and Augmentation for Code-Switched Embeddings","date":"2024-08-28","arxiv_id":"2409.00120","repositories_listed":1,"syntology":null},{"url":"/paper/gstran-joint-geometric-and-semantic-coherence","slug":"gstran-joint-geometric-and-semantic-coherence","title":"GSTran: Joint Geometric and Semantic Coherence for Point Cloud Segmentation","date":"2024-08-21","arxiv_id":"2408.11558","repositories_listed":1,"syntology":null},{"url":"/paper/distinguish-confusion-in-legal-judgment","slug":"distinguish-confusion-in-legal-judgment","title":"Distinguish Confusion in Legal Judgment Prediction via Revised Relation Knowledge","date":"2024-08-18","arxiv_id":"2408.09422","repositories_listed":1,"syntology":null},{"url":"/paper/textit-re-cse-portable-reshaping-features-for","slug":"textit-re-cse-portable-reshaping-features-for","title":"reCSE: Portable Reshaping Features for Sentence Embedding in Self-supervised Contrastive Learning","date":"2024-08-09","arxiv_id":"2408.04975","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-episode-detection-for-large","slug":"unsupervised-episode-detection-for-large","title":"Unsupervised Episode Detection for Large-Scale News Events","date":"2024-08-09","arxiv_id":"2408.04873","repositories_listed":1,"syntology":null},{"url":"/paper/semantics-or-spelling-probing-contextual-word","slug":"semantics-or-spelling-probing-contextual-word","title":"Semantics or spelling? Probing contextual word embeddings with orthographic noise","date":"2024-08-08","arxiv_id":"2408.04162","repositories_listed":1,"syntology":null},{"url":"/paper/2408-01076","slug":"2408-01076","title":"Exploiting the Semantic Knowledge of Pre-trained Text-Encoders for Continual Learning","date":"2024-08-02","arxiv_id":"2408.01076","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/2408-01076#ran","syntology_url":"https://syntology.ai/paper/2408.01076","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.01076"}},"official":{"repos":["aprilsveryown/semantically-guided-continual-learning"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}}],"record_sha256":"52955988dfd93cb23558bfac7f9404ba019293184b552ec6748e9bf3e01e5f2c","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}