{"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/language-modelling/papers/92","list_of":"/task/language-modelling","task":"Language Modelling","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":92,"pages_in_order":177,"rows_per_page":100,"rows":[9101,9200],"of":17610,"counts":{"archive_papers_tagged":17610,"with_a_code_link":7012,"where_syntology_ran_a_sample":2428,"not_listed_spam_title":0,"listed":17610,"listed_where_code_ran":2428,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":2027,"every_run_a_failure_of_syntologys_instrument":401,"listed_with_a_run_with_no_instrument_failure":2027,"listed_every_run_a_failure_of_syntologys_instrument":401,"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/language-modelling","prev":"/task/language-modelling/papers/91","next":"/task/language-modelling/papers/93","papers":[{"url":"/paper/improving-multi-modal-large-language-model","slug":"improving-multi-modal-large-language-model","title":"Improving Multi-modal Large Language Model through Boosting Vision Capabilities","date":"2024-10-17","arxiv_id":"2410.13733","repositories_listed":0,"syntology":null},{"url":null,"slug":"instruction-driven-game-engine-a-poker-case","title":"Instruction-Driven Game Engine: A Poker Case Study","date":"2024-10-17","arxiv_id":"2410.13441","repositories_listed":0,"syntology":null},{"url":null,"slug":"llm-agent-honeypot-monitoring-ai-hacking","title":"LLM Agent Honeypot: Monitoring AI Hacking Agents in the Wild","date":"2024-10-17","arxiv_id":"2410.13919","repositories_listed":0,"syntology":null},{"url":null,"slug":"mitigating-biases-to-embrace-diversity-a","title":"Mitigating Biases to Embrace Diversity: A Comprehensive Annotation Benchmark for Toxic Language","date":"2024-10-17","arxiv_id":"2410.13313","repositories_listed":0,"syntology":null},{"url":null,"slug":"proof-flow-preliminary-study-on-generative","title":"Proof Flow: Preliminary Study on Generative Flow Network Language Model Tuning for Formal Reasoning","date":"2024-10-17","arxiv_id":"2410.13224","repositories_listed":0,"syntology":null},{"url":null,"slug":"retrieval-enhanced-named-entity-recognition","title":"Retrieval-Enhanced Named Entity Recognition","date":"2024-10-17","arxiv_id":"2410.13118","repositories_listed":0,"syntology":null},{"url":null,"slug":"slm-mod-small-language-models-surpass-llms-at","title":"SLM-Mod: Small Language Models Surpass LLMs at Content Moderation","date":"2024-10-17","arxiv_id":"2410.13155","repositories_listed":0,"syntology":null},{"url":null,"slug":"style-compress-an-llm-based-prompt","title":"Style-Compress: An LLM-Based Prompt Compression Framework Considering Task-Specific Styles","date":"2024-10-17","arxiv_id":"2410.14042","repositories_listed":0,"syntology":null},{"url":null,"slug":"text-guided-multi-property-molecular","title":"Text-Guided Multi-Property Molecular Optimization with a Diffusion Language Model","date":"2024-10-17","arxiv_id":"2410.13597","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-hybrid-intelligence-in-journalism","title":"Towards Hybrid Intelligence in Journalism: Findings and Lessons Learnt from a Collaborative Analysis of Greek Political Rhetoric by ChatGPT and Humans","date":"2024-10-17","arxiv_id":"2410.13400","repositories_listed":0,"syntology":null},{"url":null,"slug":"transformer-guided-coevolution-improved-team","title":"Transformer Guided Coevolution: Improved Team Selection in Multiagent Adversarial Team Games","date":"2024-10-17","arxiv_id":"2410.13769","repositories_listed":0,"syntology":null},{"url":null,"slug":"trust-but-verify-programmatic-vlm-evaluation","title":"Trust but Verify: Programmatic VLM Evaluation in the Wild","date":"2024-10-17","arxiv_id":"2410.13121","repositories_listed":0,"syntology":null},{"url":null,"slug":"benchmarkcards-large-language-model-and-risk","title":"BenchmarkCards: Large Language Model and Risk Reporting","date":"2024-10-16","arxiv_id":"2410.12974","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-we-reverse-in-context-knowledge-edits","title":"Can We Reverse In-Context Knowledge Edits?","date":"2024-10-16","arxiv_id":"2410.12586","repositories_listed":0,"syntology":null},{"url":"/paper/developing-question-answering-models-in-low","slug":"developing-question-answering-models-in-low","title":"Developing Question-Answering Models in Low-Resource Languages: A Case Study on Turkish Medical Texts Using Transformer-Based Approaches","date":"2024-10-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-planner-training-for-language","title":"End-to-end Planner Training for Language Modeling","date":"2024-10-16","arxiv_id":"2410.12492","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainable-moral-values-a-neuro-symbolic","title":"Explainable Moral Values: a neuro-symbolic approach to value classification","date":"2024-10-16","arxiv_id":"2410.12631","repositories_listed":0,"syntology":null},{"url":null,"slug":"helm-hierarchical-encoding-for-mrna-language","title":"HELM: Hierarchical Encoding for mRNA Language Modeling","date":"2024-10-16","arxiv_id":"2410.12459","repositories_listed":0,"syntology":null},{"url":null,"slug":"iter-ahmcl-alleviate-hallucination-for-large","title":"Iter-AHMCL: Alleviate Hallucination for Large Language Model via Iterative Model-level Contrastive Learning","date":"2024-10-16","arxiv_id":"2410.12130","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-language-model-driven-multi-agent","title":"Large Language Model-driven Multi-Agent Simulation for News Diffusion Under Different Network Structures","date":"2024-10-16","arxiv_id":"2410.13909","repositories_listed":0,"syntology":null},{"url":null,"slug":"lfosum-summarizing-long-form-opinions-with","title":"LFOSum: Summarizing Long-form Opinions with Large Language Models","date":"2024-10-16","arxiv_id":"2410.13037","repositories_listed":0,"syntology":null},{"url":null,"slug":"mechanistic-unlearning-robust-knowledge","title":"Mechanistic Unlearning: Robust Knowledge Unlearning and Editing via Mechanistic Localization","date":"2024-10-16","arxiv_id":"2410.12949","repositories_listed":0,"syntology":null},{"url":null,"slug":"medaide-towards-an-omni-medical-aide-via","title":"MedAide: Towards an Omni Medical Aide via Specialized LLM-based Multi-Agent Collaboration","date":"2024-10-16","arxiv_id":"2410.12532","repositories_listed":0,"syntology":null},{"url":null,"slug":"negative-prompt-driven-alignment-for","title":"Negative-Prompt-driven Alignment for Generative Language Model","date":"2024-10-16","arxiv_id":"2410.12194","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimizing-low-resource-language-model","title":"Optimizing Low-Resource Language Model Training: Comprehensive Analysis of Multi-Epoch, Multi-Lingual, and Two-Stage Approaches","date":"2024-10-16","arxiv_id":"2410.12325","repositories_listed":0,"syntology":null},{"url":null,"slug":"refine-on-scarce-data-retrieval-enhancement","title":"REFINE on Scarce Data: Retrieval Enhancement through Fine-Tuning via Model Fusion of Embedding Models","date":"2024-10-16","arxiv_id":"2410.12890","repositories_listed":0,"syntology":null},{"url":null,"slug":"retrieval-reasoning-large-language-model","title":"Retrieval-Reasoning Large Language Model-based Synthetic Clinical Trial Generation","date":"2024-10-16","arxiv_id":"2410.12476","repositories_listed":0,"syntology":null},{"url":null,"slug":"revisited-large-language-model-for-time","title":"Revisited Large Language Model for Time Series Analysis through Modality Alignment","date":"2024-10-16","arxiv_id":"2410.12326","repositories_listed":0,"syntology":null},{"url":null,"slug":"shapefilegpt-a-multi-agent-large-language","title":"ShapefileGPT: A Multi-Agent Large Language Model Framework for Automated Shapefile Processing","date":"2024-10-16","arxiv_id":"2410.12376","repositories_listed":0,"syntology":null},{"url":null,"slug":"styledistance-stronger-content-independent","title":"StyleDistance: Stronger Content-Independent Style Embeddings with Synthetic Parallel Examples","date":"2024-10-16","arxiv_id":"2410.12757","repositories_listed":0,"syntology":null},{"url":null,"slug":"table-llm-specialist-language-model","title":"Table-LLM-Specialist: Language Model Specialists for Tables using Iterative Generator-Validator Fine-tuning","date":"2024-10-16","arxiv_id":"2410.12164","repositories_listed":0,"syntology":null},{"url":null,"slug":"tracking-universal-features-through-fine","title":"Tracking Universal Features Through Fine-Tuning and Model Merging","date":"2024-10-16","arxiv_id":"2410.12391","repositories_listed":0,"syntology":null},{"url":null,"slug":"tuning-language-models-by-mixture-of-depths","title":"Tuning Language Models by Mixture-of-Depths Ensemble","date":"2024-10-16","arxiv_id":"2410.13077","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-of-low-shot-vision-language-model","title":"A Survey of Low-shot Vision-Language Model Adaptation via Representer Theorem","date":"2024-10-15","arxiv_id":"2410.11686","repositories_listed":0,"syntology":null},{"url":null,"slug":"emotioncaps-enhancing-audio-captioning","title":"EmotionCaps: Enhancing Audio Captioning Through Emotion-Augmented Data Generation","date":"2024-10-15","arxiv_id":"2410.12028","repositories_listed":0,"syntology":null},{"url":null,"slug":"g-designer-architecting-multi-agent","title":"G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks","date":"2024-10-15","arxiv_id":"2410.11782","repositories_listed":0,"syntology":null},{"url":null,"slug":"largepig-your-large-language-model-is","title":"LargePiG: Your Large Language Model is Secretly a Pointer Generator","date":"2024-10-15","arxiv_id":"2410.11366","repositories_listed":0,"syntology":null},{"url":null,"slug":"light-weight-fault-tolerant-attention-for","title":"ATTNChecker: Highly-Optimized Fault Tolerant Attention for Large Language Model Training","date":"2024-10-15","arxiv_id":"2410.11720","repositories_listed":0,"syntology":null},{"url":null,"slug":"mitigating-frequency-bias-and-anisotropy-in","title":"Mitigating Frequency Bias and Anisotropy in Language Model Pre-Training with Syntactic Smoothing","date":"2024-10-15","arxiv_id":"2410.11462","repositories_listed":0,"syntology":null},{"url":null,"slug":"mochat-joints-grouped-spatio-temporal","title":"MoChat: Joints-Grouped Spatio-Temporal Grounding LLM for Multi-Turn Motion Comprehension and Description","date":"2024-10-15","arxiv_id":"2410.11404","repositories_listed":0,"syntology":null},{"url":null,"slug":"moe-pruner-pruning-mixture-of-experts-large","title":"MoE-Pruner: Pruning Mixture-of-Experts Large Language Model using the Hints from Its Router","date":"2024-10-15","arxiv_id":"2410.12013","repositories_listed":0,"syntology":null},{"url":null,"slug":"o-edit-orthogonal-subspace-editing-for","title":"O-Edit: Orthogonal Subspace Editing for Language Model Sequential Editing","date":"2024-10-15","arxiv_id":"2410.11469","repositories_listed":0,"syntology":null},{"url":null,"slug":"pavlm-advancing-point-cloud-based-affordance","title":"PAVLM: Advancing Point Cloud based Affordance Understanding Via Vision-Language Model","date":"2024-10-15","arxiv_id":"2410.11564","repositories_listed":0,"syntology":null},{"url":null,"slug":"retrieval-augmented-spelling-correction-for-e","title":"Retrieval Augmented Spelling Correction for E-Commerce Applications","date":"2024-10-15","arxiv_id":"2410.11655","repositories_listed":0,"syntology":null},{"url":null,"slug":"sabia-3-technical-report","title":"Sabiá-3 Technical Report","date":"2024-10-15","arxiv_id":"2410.12049","repositories_listed":0,"syntology":null},{"url":null,"slug":"sequential-llm-framework-for-fashion","title":"Sequential LLM Framework for Fashion Recommendation","date":"2024-10-15","arxiv_id":"2410.11327","repositories_listed":0,"syntology":null},{"url":null,"slug":"sgedit-bridging-llm-with-text2image","title":"SGEdit: Bridging LLM with Text2Image Generative Model for Scene Graph-based Image Editing","date":"2024-10-15","arxiv_id":"2410.11815","repositories_listed":0,"syntology":null},{"url":"/paper/shakti-a-2-5-billion-parameter-small-language","slug":"shakti-a-2-5-billion-parameter-small-language","title":"SHAKTI: A 2.5 Billion Parameter Small Language Model Optimized for Edge AI and Low-Resource Environments","date":"2024-10-15","arxiv_id":"2410.11331","repositories_listed":0,"syntology":null},{"url":null,"slug":"synthetic-interlocutors-experiments-with","title":"Synthetic Interlocutors. Experiments with Generative AI to Prolong Ethnographic Encounters","date":"2024-10-15","arxiv_id":"2410.11395","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-fair-language-model-paradox","title":"The Fair Language Model Paradox","date":"2024-10-15","arxiv_id":"2410.11985","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-moral-case-for-using-language-model","title":"The Moral Case for Using Language Model Agents for Recommendation","date":"2024-10-15","arxiv_id":"2410.12123","repositories_listed":0,"syntology":null},{"url":null,"slug":"to-err-is-ai-a-case-study-informing-llm-flaw","title":"To Err is AI : A Case Study Informing LLM Flaw Reporting Practices","date":"2024-10-15","arxiv_id":"2410.12104","repositories_listed":0,"syntology":null},{"url":null,"slug":"tokenization-and-morphology-in-multilingual","title":"Tokenization and Morphology in Multilingual Language Models: A Comparative Analysis of mT5 and ByT5","date":"2024-10-15","arxiv_id":"2410.11627","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-more-effective-table-to-text","title":"Towards More Effective Table-to-Text Generation: Assessing In-Context Learning and Self-Evaluation with Open-Source Models","date":"2024-10-15","arxiv_id":"2410.12878","repositories_listed":0,"syntology":null},{"url":null,"slug":"y-mol-a-multiscale-biomedical-knowledge","title":"Y-Mol: A Multiscale Biomedical Knowledge-Guided Large Language Model for Drug Development","date":"2024-10-15","arxiv_id":"2410.11550","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multi-task-text-classification-pipeline","title":"A Multi-Task Text Classification Pipeline with Natural Language Explanations: A User-Centric Evaluation in Sentiment Analysis and Offensive Language Identification in Greek Tweets","date":"2024-10-14","arxiv_id":"2410.10290","repositories_listed":0,"syntology":null},{"url":null,"slug":"beyond-right-and-wrong-mitigating-cold-start","title":"Not All Options Are Created Equal: Textual Option Weighting for Token-Efficient LLM-Based Knowledge Tracing","date":"2024-10-14","arxiv_id":"2410.12872","repositories_listed":0,"syntology":null},{"url":null,"slug":"character-aware-audio-visual-subtitling-in","title":"Character-aware audio-visual subtitling in context","date":"2024-10-14","arxiv_id":"2410.11068","repositories_listed":0,"syntology":null},{"url":null,"slug":"forgerygpt-multimodal-large-language-model","title":"ForgeryGPT: Multimodal Large Language Model For Explainable Image Forgery Detection and Localization","date":"2024-10-14","arxiv_id":"2410.10238","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-language-model-enhanced-reinforcement","title":"Large Language Model-Enhanced Reinforcement Learning for Generic Bus Holding Control Strategies","date":"2024-10-14","arxiv_id":"2410.10212","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-ground-vlms-without-forgetting","title":"Learning to Ground VLMs without Forgetting","date":"2024-10-14","arxiv_id":"2410.10491","repositories_listed":0,"syntology":null},{"url":null,"slug":"lg-cav-train-any-concept-activation-vector","title":"LG-CAV: Train Any Concept Activation Vector with Language Guidance","date":"2024-10-14","arxiv_id":"2410.10308","repositories_listed":0,"syntology":null},{"url":null,"slug":"lobg-less-overfitting-for-better","title":"LOBG:Less Overfitting for Better Generalization in Vision-Language Model","date":"2024-10-14","arxiv_id":"2410.10247","repositories_listed":0,"syntology":null},{"url":null,"slug":"model-based-differentially-private-knowledge","title":"Model-based Large Language Model Customization as Service","date":"2024-10-14","arxiv_id":"2410.10481","repositories_listed":0,"syntology":null},{"url":null,"slug":"practiq-a-practical-conversational-text-to","title":"PRACTIQ: A Practical Conversational Text-to-SQL dataset with Ambiguous and Unanswerable Queries","date":"2024-10-14","arxiv_id":"2410.11076","repositories_listed":0,"syntology":null},{"url":null,"slug":"recipe-for-zero-shot-pos-tagging-is-it-useful","title":"Recipe for Zero-shot POS Tagging: Is It Useful in Realistic Scenarios?","date":"2024-10-14","arxiv_id":"2410.10576","repositories_listed":0,"syntology":null},{"url":null,"slug":"skill-learning-using-process-mining-for-large","title":"Skill Learning Using Process Mining for Large Language Model Plan Generation","date":"2024-10-14","arxiv_id":"2410.12870","repositories_listed":0,"syntology":null},{"url":null,"slug":"unified-representation-of-genomic-and","title":"Unified Representation of Genomic and Biomedical Concepts through Multi-Task, Multi-Source Contrastive Learning","date":"2024-10-14","arxiv_id":"2410.10144","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-reasoning-and-acting-in-medical","title":"Adaptive Reasoning and Acting in Medical Language Agents","date":"2024-10-13","arxiv_id":"2410.10020","repositories_listed":0,"syntology":null},{"url":null,"slug":"collu-bench-a-benchmark-for-predicting","title":"Collu-Bench: A Benchmark for Predicting Language Model Hallucinations in Code","date":"2024-10-13","arxiv_id":"2410.09997","repositories_listed":0,"syntology":null},{"url":null,"slug":"echoprime-a-multi-video-view-informed-vision","title":"EchoPrime: A Multi-Video View-Informed Vision-Language Model for Comprehensive Echocardiography Interpretation","date":"2024-10-13","arxiv_id":"2410.09704","repositories_listed":0,"syntology":null},{"url":null,"slug":"generating-driving-simulations-via","title":"Conversational Code Generation: a Case Study of Designing a Dialogue System for Generating Driving Scenarios for Testing Autonomous Vehicles","date":"2024-10-13","arxiv_id":"2410.09829","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-rank-for-multiple-retrieval","title":"Learning to Rank for Multiple Retrieval-Augmented Models through Iterative Utility Maximization","date":"2024-10-13","arxiv_id":"2410.09942","repositories_listed":0,"syntology":null},{"url":null,"slug":"lore-logit-ranked-retriever-ensemble-for","title":"LoRE: Logit-Ranked Retriever Ensemble for Enhancing Open-Domain Question Answering","date":"2024-10-13","arxiv_id":"2410.10042","repositories_listed":0,"syntology":null},{"url":null,"slug":"misinfoeval-generative-ai-in-the-era-of","title":"MisinfoEval: Generative AI in the Era of \"Alternative Facts\"","date":"2024-10-13","arxiv_id":"2410.09949","repositories_listed":0,"syntology":null},{"url":null,"slug":"moin-mixture-of-introvert-experts-to-upcycle","title":"MoIN: Mixture of Introvert Experts to Upcycle an LLM","date":"2024-10-13","arxiv_id":"2410.09687","repositories_listed":0,"syntology":null},{"url":null,"slug":"extended-japanese-commonsense-morality","title":"Extended Japanese Commonsense Morality Dataset with Masked Token and Label Enhancement","date":"2024-10-12","arxiv_id":"2410.09564","repositories_listed":0,"syntology":null},{"url":null,"slug":"impeding-llm-assisted-cheating-in","title":"Impeding LLM-assisted Cheating in Introductory Programming Assignments via Adversarial Perturbation","date":"2024-10-12","arxiv_id":"2410.09318","repositories_listed":0,"syntology":null},{"url":null,"slug":"acer-automatic-language-model-context","title":"ACER: Automatic Language Model Context Extension via Retrieval","date":"2024-10-11","arxiv_id":"2410.09141","repositories_listed":0,"syntology":null},{"url":null,"slug":"aerial-vision-and-language-navigation-via","title":"Aerial Vision-and-Language Navigation via Semantic-Topo-Metric Representation Guided LLM Reasoning","date":"2024-10-11","arxiv_id":"2410.08500","repositories_listed":0,"syntology":null},{"url":null,"slug":"calibrated-cache-model-for-few-shot-vision","title":"Calibrated Cache Model for Few-Shot Vision-Language Model Adaptation","date":"2024-10-11","arxiv_id":"2410.08895","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficiently-scanning-and-resampling-spatio","title":"Efficiently Scanning and Resampling Spatio-Temporal Tasks with Irregular Observations","date":"2024-10-11","arxiv_id":"2410.08681","repositories_listed":0,"syntology":null},{"url":null,"slug":"forall-uto-exists-lor-land-l-autonomous","title":"$\\forall$uto$\\exists$$\\lor\\!\\land$L: Autonomous Evaluation of LLMs for Truth Maintenance and Reasoning Tasks","date":"2024-10-11","arxiv_id":"2410.08437","repositories_listed":0,"syntology":null},{"url":null,"slug":"hypothesis-only-biases-in-large-language","title":"Hypothesis-only Biases in Large Language Model-Elicited Natural Language Inference","date":"2024-10-11","arxiv_id":"2410.08996","repositories_listed":0,"syntology":null},{"url":null,"slug":"language-model-assisted-bi-level-programming","title":"Language-Model-Assisted Bi-Level Programming for Reward Learning from Internet Videos","date":"2024-10-11","arxiv_id":"2410.09286","repositories_listed":0,"syntology":null},{"url":null,"slug":"lifelong-event-detection-via-optimal","title":"Lifelong Event Detection via Optimal Transport","date":"2024-10-11","arxiv_id":"2410.08905","repositories_listed":0,"syntology":null},{"url":null,"slug":"llmd-a-large-language-model-for-interpreting","title":"LLMD: A Large Language Model for Interpreting Longitudinal Medical Records","date":"2024-10-11","arxiv_id":"2410.12860","repositories_listed":0,"syntology":null},{"url":null,"slug":"nach0-pc-multi-task-language-model-with","title":"nach0-pc: Multi-task Language Model with Molecular Point Cloud Encoder","date":"2024-10-11","arxiv_id":"2410.09240","repositories_listed":0,"syntology":null},{"url":null,"slug":"preferential-normalizing-flows","title":"Preferential Normalizing Flows","date":"2024-10-11","arxiv_id":"2410.08710","repositories_listed":0,"syntology":null},{"url":null,"slug":"simplestrat-diversifying-language-model","title":"SimpleStrat: Diversifying Language Model Generation with Stratification","date":"2024-10-11","arxiv_id":"2410.09038","repositories_listed":0,"syntology":null},{"url":null,"slug":"simultaneous-reward-distillation-and","title":"Simultaneous Reward Distillation and Preference Learning: Get You a Language Model Who Can Do Both","date":"2024-10-11","arxiv_id":"2410.08458","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-dynamics-of-social-conventions-in-llm","title":"Emergent social conventions and collective bias in LLM populations","date":"2024-10-11","arxiv_id":"2410.08948","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-same-but-different-structural","title":"The Same But Different: Structural Similarities and Differences in Multilingual Language Modeling","date":"2024-10-11","arxiv_id":"2410.09223","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-trustworthy-knowledge-graph-reasoning","title":"Towards Trustworthy Knowledge Graph Reasoning: An Uncertainty Aware Perspective","date":"2024-10-11","arxiv_id":"2410.08985","repositories_listed":0,"syntology":null},{"url":null,"slug":"vit3d-alignment-of-llama3-3d-medical-image","title":"ViT3D Alignment of LLaMA3: 3D Medical Image Report Generation","date":"2024-10-11","arxiv_id":"2410.08588","repositories_listed":0,"syntology":null},{"url":null,"slug":"vlm-see-robot-do-human-demo-video-to-robot","title":"VLM See, Robot Do: Human Demo Video to Robot Action Plan via Vision Language Model","date":"2024-10-11","arxiv_id":"2410.08792","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-framework-for-collaborating-a-large","title":"A Framework for Collaborating a Large Language Model Tool in Brainstorming for Triggering Creative Thoughts","date":"2024-10-10","arxiv_id":"2410.11877","repositories_listed":0,"syntology":null},{"url":null,"slug":"animating-the-past-reconstruct-trilobite-via","title":"Animating the Past: Reconstruct Trilobite via Video Generation","date":"2024-10-10","arxiv_id":"2410.14715","repositories_listed":0,"syntology":null},{"url":null,"slug":"crossquant-a-post-training-quantization","title":"CrossQuant: A Post-Training Quantization Method with Smaller Quantization Kernel for Precise Large Language Model Compression","date":"2024-10-10","arxiv_id":"2410.07505","repositories_listed":0,"syntology":null},{"url":null,"slug":"dice-discrete-inversion-enabling-controllable","title":"DICE: Discrete Inversion Enabling Controllable Editing for Multinomial Diffusion and Masked Generative Models","date":"2024-10-10","arxiv_id":"2410.08207","repositories_listed":0,"syntology":null}],"record_sha256":"f1e200bde775bca79ee8da996a6571250d42e360c76371db278fb1ac1d198076","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}