{"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":"/method/dense-connections/papers/144","list_of":"/method/dense-connections","method":"Dense Connections","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":144,"pages_in_order":293,"rows_per_page":100,"rows":[14301,14400],"of":29230,"counts":{"archive_papers_tagged":29230,"with_a_code_link":12972,"where_syntology_ran_a_sample":3929,"not_listed_spam_title":0,"listed":29230,"listed_where_code_ran":3929,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":3303,"every_run_a_failure_of_syntologys_instrument":626,"listed_with_a_run_with_no_instrument_failure":3303,"listed_every_run_a_failure_of_syntologys_instrument":626,"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":"/method/dense-connections","prev":"/method/dense-connections/papers/143","next":"/method/dense-connections/papers/145","papers":[{"paper":"/paper/future-conditioned-unsupervised-pretraining","slug":"future-conditioned-unsupervised-pretraining","title":"Future-conditioned Unsupervised Pretraining for Decision Transformer","date":"2023-05-26","arxiv_id":"2305.16683","n_code_links":1,"syntology":{"ran":5,"of":7,"n_ran_checked":5,"n_instrument":0,"unverified":2,"pointer_only":1,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["fffffarmer/pdt"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/geovln-learning-geometry-enhanced-visual-1","slug":"geovln-learning-geometry-enhanced-visual-1","title":"GeoVLN: Learning Geometry-Enhanced Visual Representation with Slot Attention for Vision-and-Language Navigation","date":"2023-05-26","arxiv_id":"2305.17102","n_code_links":1,"syntology":null},{"paper":null,"slug":"impossible-distillation-from-low-quality","title":"Impossible Distillation: from Low-Quality Model to High-Quality Dataset & Model for Summarization and Paraphrasing","date":"2023-05-26","arxiv_id":"2305.16635","n_code_links":0,"syntology":null},{"paper":null,"slug":"improving-accuracy-of-gpt-3-4-results-on","title":"Improving accuracy of GPT-3/4 results on biomedical data using a retrieval-augmented language model","date":"2023-05-26","arxiv_id":"2305.17116","n_code_links":0,"syntology":null},{"paper":"/paper/improving-position-encoding-of-transformers","slug":"improving-position-encoding-of-transformers","title":"Improving Position Encoding of Transformers for Multivariate Time Series Classification","date":"2023-05-26","arxiv_id":"2305.16642","n_code_links":1,"syntology":null},{"paper":null,"slug":"incorporating-distributions-of-discourse","title":"Incorporating Distributions of Discourse Structure for Long Document Abstractive Summarization","date":"2023-05-26","arxiv_id":"2305.16784","n_code_links":0,"syntology":null},{"paper":null,"slug":"knse-a-knowledge-aware-natural-language","title":"KNSE: A Knowledge-aware Natural Language Inference Framework for Dialogue Symptom Status Recognition","date":"2023-05-26","arxiv_id":"2305.16833","n_code_links":0,"syntology":null},{"paper":"/paper/large-language-models-as-tool-makers","slug":"large-language-models-as-tool-makers","title":"Large Language Models as Tool Makers","date":"2023-05-26","arxiv_id":"2305.17126","n_code_links":1,"syntology":null},{"paper":null,"slug":"learning-and-leveraging-verifiers-to-improve","title":"Learning and Leveraging Verifiers to Improve Planning Capabilities of Pre-trained Language Models","date":"2023-05-26","arxiv_id":"2305.17077","n_code_links":0,"syntology":null},{"paper":"/paper/learning-to-imagine-visually-augmented","slug":"learning-to-imagine-visually-augmented","title":"Learning to Imagine: Visually-Augmented Natural Language Generation","date":"2023-05-26","arxiv_id":"2305.16944","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"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) · 0 unverified","official":{"repos":["rucaibox/live"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["found_in_text"]}}},{"paper":"/paper/llms-and-the-abstraction-and-reasoning-corpus","slug":"llms-and-the-abstraction-and-reasoning-corpus","title":"LLMs and the Abstraction and Reasoning Corpus: Successes, Failures, and the Importance of Object-based Representations","date":"2023-05-26","arxiv_id":"2305.18354","n_code_links":1,"syntology":null},{"paper":"/paper/navgpt-explicit-reasoning-in-vision-and","slug":"navgpt-explicit-reasoning-in-vision-and","title":"NavGPT: Explicit Reasoning in Vision-and-Language Navigation with Large Language Models","date":"2023-05-26","arxiv_id":"2305.16986","n_code_links":2,"syntology":{"ran":4,"of":5,"n_ran_checked":2,"n_instrument":2,"unverified":1,"pointer_only":2,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","official":{"repos":["gengzezhou/navgpt"],"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":["listed","official"]}}},{"paper":"/paper/neural-task-synthesis-for-visual-programming","slug":"neural-task-synthesis-for-visual-programming","title":"Neural Task Synthesis for Visual Programming","date":"2023-05-26","arxiv_id":"2305.18342","n_code_links":1,"syntology":null},{"paper":"/paper/on-evaluating-adversarial-robustness-of-large","slug":"on-evaluating-adversarial-robustness-of-large","title":"On Evaluating Adversarial Robustness of Large Vision-Language Models","date":"2023-05-26","arxiv_id":"2305.16934","n_code_links":1,"syntology":{"ran":4,"of":4,"n_ran_checked":2,"n_instrument":2,"unverified":0,"pointer_only":2,"phrase":"4 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; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["yunqing-me/attackvlm"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"playing-repeated-games-with-large-language","title":"Playing repeated games with Large Language Models","date":"2023-05-26","arxiv_id":"2305.16867","n_code_links":0,"syntology":null},{"paper":null,"slug":"stylehumanclip-text-guided-garment","title":"StyleHumanCLIP: Text-guided Garment Manipulation for StyleGAN-Human","date":"2023-05-26","arxiv_id":"2305.16759","n_code_links":0,"syntology":null},{"paper":null,"slug":"thailand-asset-value-estimation-using-aerial","title":"Thailand Asset Value Estimation Using Aerial or Satellite Imagery","date":"2023-05-26","arxiv_id":"2307.08650","n_code_links":0,"syntology":null},{"paper":null,"slug":"theoretical-and-practical-perspectives-on","title":"Theoretical and Practical Perspectives on what Influence Functions Do","date":"2023-05-26","arxiv_id":"2305.16971","n_code_links":0,"syntology":null},{"paper":null,"slug":"transformer-slow-fast-transformer-for-machine","title":"TranSFormer: Slow-Fast Transformer for Machine Translation","date":"2023-05-26","arxiv_id":"2305.16982","n_code_links":0,"syntology":null},{"paper":"/paper/zero-is-not-hero-yet-benchmarking-zero-shot","slug":"zero-is-not-hero-yet-benchmarking-zero-shot","title":"Zero is Not Hero Yet: Benchmarking Zero-Shot Performance of LLMs for Financial Tasks","date":"2023-05-26","arxiv_id":"2305.16633","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-survey-on-chatgpt-ai-generated-contents","title":"A Survey on ChatGPT: AI-Generated Contents, Challenges, and Solutions","date":"2023-05-25","arxiv_id":"2305.18339","n_code_links":0,"syntology":null},{"paper":null,"slug":"asking-before-action-gather-information-in","title":"Asking Before Acting: Gather Information in Embodied Decision Making with Language Models","date":"2023-05-25","arxiv_id":"2305.15695","n_code_links":0,"syntology":null},{"paper":null,"slug":"chatgpt-for-plc-dcs-control-logic-generation","title":"ChatGPT for PLC/DCS Control Logic Generation","date":"2023-05-25","arxiv_id":"2305.15809","n_code_links":0,"syntology":null},{"paper":null,"slug":"comparative-study-of-pre-trained-bert-models","title":"Comparative Study of Pre-Trained BERT Models for Code-Mixed Hindi-English Data","date":"2023-05-25","arxiv_id":"2305.15722","n_code_links":0,"syntology":null},{"paper":"/paper/concept-centric-transformers-concept","slug":"concept-centric-transformers-concept","title":"Concept-Centric Transformers: Enhancing Model Interpretability through Object-Centric Concept Learning within a Shared Global Workspace","date":"2023-05-25","arxiv_id":"2305.15775","n_code_links":2,"syntology":null},{"paper":null,"slug":"context-aware-attention-layers-coupled-with","title":"Context-aware attention layers coupled with optimal transport domain adaptation and multimodal fusion methods for recognizing dementia from spontaneous speech","date":"2023-05-25","arxiv_id":"2305.16406","n_code_links":0,"syntology":null},{"paper":null,"slug":"cross-view-action-recognition-understanding","title":"Cross-view Action Recognition Understanding From Exocentric to Egocentric Perspective","date":"2023-05-25","arxiv_id":"2305.15699","n_code_links":0,"syntology":null},{"paper":"/paper/generatect-text-guided-3d-chest-ct-generation","slug":"generatect-text-guided-3d-chest-ct-generation","title":"GenerateCT: Text-Conditional Generation of 3D Chest CT Volumes","date":"2023-05-25","arxiv_id":"2305.16037","n_code_links":1,"syntology":{"ran":14,"of":15,"n_ran_checked":13,"n_instrument":1,"unverified":1,"pointer_only":5,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 1 honoured, 4 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["ibrahimethemhamamci/generatect"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":13,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"imitating-task-and-motion-planning-with","title":"Imitating Task and Motion Planning with Visuomotor Transformers","date":"2023-05-25","arxiv_id":"2305.16309","n_code_links":0,"syntology":null},{"paper":"/paper/landmark-attention-random-access-infinite","slug":"landmark-attention-random-access-infinite","title":"Landmark Attention: Random-Access Infinite Context Length for Transformers","date":"2023-05-25","arxiv_id":"2305.16300","n_code_links":2,"syntology":{"ran":11,"of":13,"n_ran_checked":5,"n_instrument":6,"unverified":2,"pointer_only":0,"phrase":"11 ran (of which 5 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 6 where Syntology's instrument failed) · 2 unverified","official":{"repos":["epfml/landmark-attention"],"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":["community","listed","official"]}}},{"paper":"/paper/linguistic-properties-of-truthful-response","slug":"linguistic-properties-of-truthful-response","title":"Linguistic Properties of Truthful Response","date":"2023-05-25","arxiv_id":"2305.15875","n_code_links":1,"syntology":null},{"paper":null,"slug":"making-vision-transformers-truly-shift","title":"Making Vision Transformers Truly Shift-Equivariant","date":"2023-05-25","arxiv_id":"2305.16316","n_code_links":0,"syntology":null},{"paper":null,"slug":"mask-attack-detection-using-vascular-weighted","title":"Mask Attack Detection Using Vascular-weighted Motion-robust rPPG Signals","date":"2023-05-25","arxiv_id":"2305.15940","n_code_links":0,"syntology":null},{"paper":"/paper/merge-fast-private-text-generation","slug":"merge-fast-private-text-generation","title":"MERGE: Fast Private Text Generation","date":"2023-05-25","arxiv_id":"2305.15769","n_code_links":1,"syntology":null},{"paper":null,"slug":"multi-scale-efficient-graph-transformer-for","title":"Multi-scale Efficient Graph-Transformer for Whole Slide Image Classification","date":"2023-05-25","arxiv_id":"2305.15773","n_code_links":0,"syntology":null},{"paper":"/paper/multilingual-text-to-speech-synthesis-for","slug":"multilingual-text-to-speech-synthesis-for","title":"Multilingual Text-to-Speech Synthesis for Turkic Languages Using Transliteration","date":"2023-05-25","arxiv_id":"2305.15749","n_code_links":1,"syntology":null},{"paper":"/paper/nextou-efficient-topology-aware-u-net-for","slug":"nextou-efficient-topology-aware-u-net-for","title":"NexToU: Efficient Topology-Aware U-Net for Medical Image Segmentation","date":"2023-05-25","arxiv_id":"2305.15911","n_code_links":2,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"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) · 0 unverified","official":{"repos":["pengchengshi1220/nextou"],"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":["listed","official"]}}},{"paper":null,"slug":"not-wacky-vs-definitely-wacky-a-study-of","title":"Not wacky vs. definitely wacky: A study of scalar adverbs in pretrained language models","date":"2023-05-25","arxiv_id":"2305.16426","n_code_links":0,"syntology":null},{"paper":"/paper/on-the-tool-manipulation-capability-of-open","slug":"on-the-tool-manipulation-capability-of-open","title":"On the Tool Manipulation Capability of Open-source Large Language Models","date":"2023-05-25","arxiv_id":"2305.16504","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"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) · 0 unverified","official":{"repos":["sambanova/toolbench"],"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"]}}},{"paper":"/paper/pre-training-meets-clustering-a-hybrid","slug":"pre-training-meets-clustering-a-hybrid","title":"Pre-training Meets Clustering: A Hybrid Extractive Multi-document Summarization Model","date":"2023-05-25","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/self-contradictory-hallucinations-of-large","slug":"self-contradictory-hallucinations-of-large","title":"Self-contradictory Hallucinations of Large Language Models: Evaluation, Detection and Mitigation","date":"2023-05-25","arxiv_id":"2305.15852","n_code_links":1,"syntology":{"ran":5,"of":9,"n_ran_checked":5,"n_instrument":0,"unverified":4,"pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","official":{"repos":["eth-sri/chatprotect"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"stecformer-spatio-temporal-encoding-cascaded","title":"Stecformer: Spatio-temporal Encoding Cascaded Transformer for Multivariate Long-term Time Series Forecasting","date":"2023-05-25","arxiv_id":"2305.16370","n_code_links":0,"syntology":null},{"paper":"/paper/text-to-motion-retrieval-towards-joint","slug":"text-to-motion-retrieval-towards-joint","title":"Text-to-Motion Retrieval: Towards Joint Understanding of Human Motion Data and Natural Language","date":"2023-05-25","arxiv_id":"2305.15842","n_code_links":1,"syntology":null},{"paper":null,"slug":"umat-uncertainty-aware-single-image-high-1","title":"UMat: Uncertainty-Aware Single Image High Resolution Material Capture","date":"2023-05-25","arxiv_id":"2305.16312","n_code_links":0,"syntology":null},{"paper":null,"slug":"undetectable-watermarks-for-language-models","title":"Undetectable Watermarks for Language Models","date":"2023-05-25","arxiv_id":"2306.09194","n_code_links":0,"syntology":null},{"paper":"/paper/unitrec-a-unified-text-to-text-transformer","slug":"unitrec-a-unified-text-to-text-transformer","title":"UniTRec: A Unified Text-to-Text Transformer and Joint Contrastive Learning Framework for Text-based Recommendation","date":"2023-05-25","arxiv_id":"2305.15756","n_code_links":1,"syntology":{"ran":8,"of":11,"n_ran_checked":8,"n_instrument":0,"unverified":3,"pointer_only":6,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","official":{"repos":["veason-silverbullet/unitrec"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"viola-unified-codec-language-models-for","title":"VioLA: Unified Codec Language Models for Speech Recognition, Synthesis, and Translation","date":"2023-05-25","arxiv_id":"2305.16107","n_code_links":0,"syntology":null},{"paper":"/paper/voyager-an-open-ended-embodied-agent-with","slug":"voyager-an-open-ended-embodied-agent-with","title":"Voyager: An Open-Ended Embodied Agent with Large Language Models","date":"2023-05-25","arxiv_id":"2305.16291","n_code_links":1,"syntology":{"ran":5,"of":6,"n_ran_checked":5,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["MineDojo/Voyager"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/2305-14649","slug":"2305-14649","title":"A Joint Time-frequency Domain Transformer for Multivariate Time Series Forecasting","date":"2023-05-24","arxiv_id":"2305.14649","n_code_links":1,"syntology":null},{"paper":"/paper/2305-14675","slug":"2305-14675","title":"TriMLP: Revenge of a MLP-like Architecture in Sequential Recommendation","date":"2023-05-24","arxiv_id":"2305.14675","n_code_links":1,"syntology":null},{"paper":"/paper/a-causal-view-of-entity-bias-in-large","slug":"a-causal-view-of-entity-bias-in-large","title":"A Causal View of Entity Bias in (Large) Language Models","date":"2023-05-24","arxiv_id":"2305.14695","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":1,"n_instrument":1,"unverified":0,"pointer_only":0,"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","official":{"repos":["luka-group/causal-view-of-entity-bias"],"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"]}}},{"paper":"/paper/a-new-era-in-software-security-towards-self","slug":"a-new-era-in-software-security-towards-self","title":"A New Era in Software Security: Towards Self-Healing Software via Large Language Models and Formal Verification","date":"2023-05-24","arxiv_id":"2305.14752","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-relentless-benchmark-for-modelling-graded","title":"A RelEntLess Benchmark for Modelling Graded Relations between Named Entities","date":"2023-05-24","arxiv_id":"2305.15002","n_code_links":0,"syntology":null},{"paper":null,"slug":"adversarial-demonstration-attacks-on-large","title":"Adversarial Demonstration Attacks on Large Language Models","date":"2023-05-24","arxiv_id":"2305.14950","n_code_links":0,"syntology":null},{"paper":null,"slug":"benchmarking-arabic-ai-with-large-language","title":"LAraBench: Benchmarking Arabic AI with Large Language Models","date":"2023-05-24","arxiv_id":"2305.14982","n_code_links":0,"syntology":null},{"paper":"/paper/bytesized32-a-corpus-and-challenge-task-for","slug":"bytesized32-a-corpus-and-challenge-task-for","title":"ByteSized32: A Corpus and Challenge Task for Generating Task-Specific World Models Expressed as Text Games","date":"2023-05-24","arxiv_id":"2305.14879","n_code_links":1,"syntology":{"ran":6,"of":8,"n_ran_checked":6,"n_instrument":0,"unverified":2,"pointer_only":0,"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) · 2 unverified","official":{"repos":["cognitiveailab/bytesized32"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"chain-of-questions-training-with-latent","title":"Chain-of-Questions Training with Latent Answers for Robust Multistep Question Answering","date":"2023-05-24","arxiv_id":"2305.14901","n_code_links":0,"syntology":null},{"paper":"/paper/chatagri-exploring-potentials-of-chatgpt-on","slug":"chatagri-exploring-potentials-of-chatgpt-on","title":"ChatAgri: Exploring Potentials of ChatGPT on Cross-linguistic Agricultural Text Classification","date":"2023-05-24","arxiv_id":"2305.15024","n_code_links":1,"syntology":null},{"paper":null,"slug":"clever-hans-or-neural-theory-of-mind-stress","title":"Clever Hans or Neural Theory of Mind? Stress Testing Social Reasoning in Large Language Models","date":"2023-05-24","arxiv_id":"2305.14763","n_code_links":0,"syntology":null},{"paper":"/paper/complex-mathematical-symbol-definition","slug":"complex-mathematical-symbol-definition","title":"Complex Mathematical Symbol Definition Structures: A Dataset and Model for Coordination Resolution in Definition Extraction","date":"2023-05-24","arxiv_id":"2305.14660","n_code_links":1,"syntology":null},{"paper":"/paper/context-aware-transformer-pre-training-for","slug":"context-aware-transformer-pre-training-for","title":"Context-Aware Transformer Pre-Training for Answer Sentence Selection","date":"2023-05-24","arxiv_id":"2305.15358","n_code_links":0,"syntology":null},{"paper":null,"slug":"diffusion-models-in-nlp-a-survey-1","title":"A Survey of Diffusion Models in Natural Language Processing","date":"2023-05-24","arxiv_id":"2305.14671","n_code_links":0,"syntology":null},{"paper":null,"slug":"don-t-take-this-out-of-context-on-the-need","title":"Don't Take This Out of Context! On the Need for Contextual Models and Evaluations for Stylistic Rewriting","date":"2023-05-24","arxiv_id":"2305.14755","n_code_links":0,"syntology":null},{"paper":null,"slug":"don-t-trust-gpt-when-your-question-is-not-in","title":"Don't Trust ChatGPT when Your Question is not in English: A Study of Multilingual Abilities and Types of LLMs","date":"2023-05-24","arxiv_id":"2305.16339","n_code_links":0,"syntology":null},{"paper":null,"slug":"dynamic-masking-rate-schedules-for-mlm","title":"Dynamic Masking Rate Schedules for MLM Pretraining","date":"2023-05-24","arxiv_id":"2305.15096","n_code_links":0,"syntology":null},{"paper":"/paper/editing-commonsense-knowledge-in-gpt","slug":"editing-commonsense-knowledge-in-gpt","title":"Editing Common Sense in Transformers","date":"2023-05-24","arxiv_id":"2305.14956","n_code_links":1,"syntology":{"ran":6,"of":10,"n_ran_checked":6,"n_instrument":0,"unverified":4,"pointer_only":0,"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) · 4 unverified","official":{"repos":["anshitag/memit_csk"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":"/paper/expertprompting-instructing-large-language","slug":"expertprompting-instructing-large-language","title":"ExpertPrompting: Instructing Large Language Models to be Distinguished Experts","date":"2023-05-24","arxiv_id":"2305.14688","n_code_links":2,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"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) · 0 unverified","official":{"repos":["ofa-sys/expertllama"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":null,"slug":"extracting-psychological-indicators-using","title":"Extracting Psychological Indicators Using Question Answering","date":"2023-05-24","arxiv_id":"2305.14891","n_code_links":0,"syntology":null},{"paper":"/paper/fourier-transformer-fast-long-range-modeling","slug":"fourier-transformer-fast-long-range-modeling","title":"Fourier Transformer: Fast Long Range Modeling by Removing Sequence Redundancy with FFT Operator","date":"2023-05-24","arxiv_id":"2305.15099","n_code_links":1,"syntology":null},{"paper":"/paper/from-words-to-wires-generating-functioning","slug":"from-words-to-wires-generating-functioning","title":"From Words to Wires: Generating Functioning Electronic Devices from Natural Language Descriptions","date":"2023-05-24","arxiv_id":"2305.14874","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":0,"n_instrument":3,"unverified":0,"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","official":{"repos":["cognitiveailab/words2wires"],"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"]}}},{"paper":"/paper/ghostbuster-detecting-text-ghostwritten-by","slug":"ghostbuster-detecting-text-ghostwritten-by","title":"Ghostbuster: Detecting Text Ghostwritten by Large Language Models","date":"2023-05-24","arxiv_id":"2305.15047","n_code_links":2,"syntology":null},{"paper":"/paper/gorilla-large-language-model-connected-with","slug":"gorilla-large-language-model-connected-with","title":"Gorilla: Large Language Model Connected with Massive APIs","date":"2023-05-24","arxiv_id":"2305.15334","n_code_links":1,"syntology":null},{"paper":null,"slug":"gptaraeval-a-comprehensive-evaluation-of","title":"GPTAraEval: A Comprehensive Evaluation of ChatGPT on Arabic NLP","date":"2023-05-24","arxiv_id":"2305.14976","n_code_links":0,"syntology":null},{"paper":null,"slug":"gtnet-graph-transformer-network-for-3d-point","title":"GTNet: Graph Transformer Network for 3D Point Cloud Classification and Semantic Segmentation","date":"2023-05-24","arxiv_id":"2305.15213","n_code_links":0,"syntology":null},{"paper":"/paper/harnessing-the-power-of-large-language-models","slug":"harnessing-the-power-of-large-language-models","title":"Harnessing the Power of Large Language Models for Natural Language to First-Order Logic Translation","date":"2023-05-24","arxiv_id":"2305.15541","n_code_links":1,"syntology":{"ran":5,"of":5,"n_ran_checked":5,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["gblackout/logicllama"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/have-llms-advanced-enough-a-challenging","slug":"have-llms-advanced-enough-a-challenging","title":"Have LLMs Advanced Enough? A Challenging Problem Solving Benchmark For Large Language Models","date":"2023-05-24","arxiv_id":"2305.15074","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"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","official":{"repos":["hgaurav2k/jeebench"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["found_in_text"]}}},{"paper":"/paper/how-to-distill-your-bert-an-empirical-study","slug":"how-to-distill-your-bert-an-empirical-study","title":"How to Distill your BERT: An Empirical Study on the Impact of Weight Initialisation and Distillation Objectives","date":"2023-05-24","arxiv_id":"2305.15032","n_code_links":1,"syntology":{"ran":6,"of":11,"n_ran_checked":5,"n_instrument":1,"unverified":5,"pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 5 unverified","official":{"repos":["mainlp/how-to-distill-your-bert"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":5,"ran_from_kinds":["official"]}}},{"paper":"/paper/huatuogpt-towards-taming-language-model-to-be","slug":"huatuogpt-towards-taming-language-model-to-be","title":"HuatuoGPT, towards Taming Language Model to Be a Doctor","date":"2023-05-24","arxiv_id":"2305.15075","n_code_links":2,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"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","official":{"repos":["freedomintelligence/huatuogpt"],"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"]}}},{"paper":null,"slug":"human-centered-metrics-for-dialog-system","title":"Psychological Metrics for Dialog System Evaluation","date":"2023-05-24","arxiv_id":"2305.14757","n_code_links":0,"syntology":null},{"paper":"/paper/i-spy-a-metaphor-large-language-models-and","slug":"i-spy-a-metaphor-large-language-models-and","title":"I Spy a Metaphor: Large Language Models and Diffusion Models Co-Create Visual Metaphors","date":"2023-05-24","arxiv_id":"2305.14724","n_code_links":1,"syntology":null},{"paper":"/paper/inference-time-policy-adapters-ipa-tailoring","slug":"inference-time-policy-adapters-ipa-tailoring","title":"Inference-Time Policy Adapters (IPA): Tailoring Extreme-Scale LMs without Fine-tuning","date":"2023-05-24","arxiv_id":"2305.15065","n_code_links":1,"syntology":{"ran":9,"of":11,"n_ran_checked":5,"n_instrument":4,"unverified":2,"pointer_only":0,"phrase":"9 ran (of which 3 constructed an object rather than computing a result; 5 with no instrument failure: 2 honoured, 0 violated, 3 with no contract checked; 4 where Syntology's instrument failed) · 2 unverified","official":{"repos":["gximinglu/ipa"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":3,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"interformer-interactive-local-and-global","title":"InterFormer: Interactive Local and Global Features Fusion for Automatic Speech Recognition","date":"2023-05-24","arxiv_id":"2305.16342","n_code_links":0,"syntology":null},{"paper":"/paper/is-gpt-4-a-good-data-analyst","slug":"is-gpt-4-a-good-data-analyst","title":"Is GPT-4 a Good Data Analyst?","date":"2023-05-24","arxiv_id":"2305.15038","n_code_links":1,"syntology":null},{"paper":"/paper/just-ask-for-calibration-strategies-for","slug":"just-ask-for-calibration-strategies-for","title":"Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback","date":"2023-05-24","arxiv_id":"2305.14975","n_code_links":1,"syntology":null},{"paper":null,"slug":"knn-lm-does-not-improve-open-ended-text","title":"KNN-LM Does Not Improve Open-ended Text Generation","date":"2023-05-24","arxiv_id":"2305.14625","n_code_links":0,"syntology":null},{"paper":"/paper/large-language-models-are-effective-table-to","slug":"large-language-models-are-effective-table-to","title":"Investigating Table-to-Text Generation Capabilities of LLMs in Real-World Information Seeking Scenarios","date":"2023-05-24","arxiv_id":"2305.14987","n_code_links":2,"syntology":null},{"paper":null,"slug":"leveraging-gpt-4-for-automatic-translation","title":"Leveraging GPT-4 for Automatic Translation Post-Editing","date":"2023-05-24","arxiv_id":"2305.14878","n_code_links":0,"syntology":null},{"paper":null,"slug":"leveraging-llms-for-kpis-retrieval-from","title":"Enabling and Analyzing How to Efficiently Extract Information from Hybrid Long Documents with LLMs","date":"2023-05-24","arxiv_id":"2305.16344","n_code_links":0,"syntology":null},{"paper":null,"slug":"leveraging-pre-trained-large-language-models","title":"Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning","date":"2023-05-24","arxiv_id":"2305.14909","n_code_links":0,"syntology":null},{"paper":"/paper/llmdet-a-large-language-models-detection-tool","slug":"llmdet-a-large-language-models-detection-tool","title":"LLMDet: A Third Party Large Language Models Generated Text Detection Tool","date":"2023-05-24","arxiv_id":"2305.15004","n_code_links":1,"syntology":{"ran":1,"of":3,"n_ran_checked":0,"n_instrument":1,"unverified":2,"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) · 2 unverified","official":{"repos":["trustedllm/llmdet"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"mastering-the-abcds-of-complex-questions","title":"Mastering the ABCDs of Complex Questions: Answer-Based Claim Decomposition for Fine-grained Self-Evaluation","date":"2023-05-24","arxiv_id":"2305.14750","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-modal-mutual-attention-and-iterative","title":"Multi-Modal Mutual Attention and Iterative Interaction for Referring Image Segmentation","date":"2023-05-24","arxiv_id":"2305.15302","n_code_links":0,"syntology":null},{"paper":null,"slug":"multiresolution-feature-guidance-based","title":"Multiresolution Feature Guidance Based Transformer for Anomaly Detection","date":"2023-05-24","arxiv_id":"2305.14880","n_code_links":0,"syntology":null},{"paper":null,"slug":"neural-summarization-of-electronic-health","title":"Neural Summarization of Electronic Health Records","date":"2023-05-24","arxiv_id":"2305.15222","n_code_links":0,"syntology":null},{"paper":null,"slug":"p-vectors-a-parallel-coupled-tdnn-transformer","title":"P-vectors: A Parallel-Coupled TDNN/Transformer Network for Speaker Verification","date":"2023-05-24","arxiv_id":"2305.14778","n_code_links":0,"syntology":null},{"paper":"/paper/peek-across-improving-multi-document-modeling","slug":"peek-across-improving-multi-document-modeling","title":"Peek Across: Improving Multi-Document Modeling via Cross-Document Question-Answering","date":"2023-05-24","arxiv_id":"2305.15387","n_code_links":1,"syntology":{"ran":12,"of":18,"n_ran_checked":11,"n_instrument":1,"unverified":6,"pointer_only":0,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 1 where Syntology's instrument failed) · 6 unverified","official":{"repos":["aviclu/peekacross"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":6,"ran_from_kinds":["official"]}}},{"paper":"/paper/pre-rmsnorm-and-pre-crmsnorm-transformers","slug":"pre-rmsnorm-and-pre-crmsnorm-transformers","title":"Pre-RMSNorm and Pre-CRMSNorm Transformers: Equivalent and Efficient Pre-LN Transformers","date":"2023-05-24","arxiv_id":"2305.14858","n_code_links":1,"syntology":{"ran":0,"of":2,"n_ran_checked":0,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"0 ran · 2 unverified","official":{"repos":["zixuanjiang/pre-rmsnorm-transformer"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"paper":null,"slug":"predicting-token-impact-towards-efficient","title":"Predicting Token Impact Towards Efficient Vision Transformer","date":"2023-05-24","arxiv_id":"2305.14840","n_code_links":0,"syntology":null},{"paper":"/paper/prompt-optimization-of-large-language-model","slug":"prompt-optimization-of-large-language-model","title":"AutoPlan: Automatic Planning of Interactive Decision-Making Tasks With Large Language Models","date":"2023-05-24","arxiv_id":"2305.15064","n_code_links":1,"syntology":{"ran":2,"of":4,"n_ran_checked":1,"n_instrument":1,"unverified":2,"pointer_only":0,"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) · 2 unverified","official":{"repos":["owaski/autoplan"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/reasoning-with-language-model-is-planning","slug":"reasoning-with-language-model-is-planning","title":"Reasoning with Language Model is Planning with World Model","date":"2023-05-24","arxiv_id":"2305.14992","n_code_links":3,"syntology":{"ran":4,"of":7,"n_ran_checked":2,"n_instrument":2,"unverified":3,"pointer_only":0,"phrase":"4 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; 2 where Syntology's instrument failed) · 3 unverified","official":null}}],"record_sha256":"432d4562ec33e72f50bea8ce0c20ada70fcf33d368652ba7f194e3d7652f2986","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}