{"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/text-generation/papers/26","list_of":"/task/text-generation","task":"Text Generation","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":26,"pages_in_order":54,"rows_per_page":100,"rows":[2501,2600],"of":5335,"counts":{"archive_papers_tagged":5335,"with_a_code_link":2047,"where_syntology_ran_a_sample":610,"not_listed_spam_title":0,"listed":5335,"listed_where_code_ran":610,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":503,"every_run_a_failure_of_syntologys_instrument":107,"listed_with_a_run_with_no_instrument_failure":503,"listed_every_run_a_failure_of_syntologys_instrument":107,"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/text-generation","prev":"/task/text-generation/papers/25","next":"/task/text-generation/papers/27","papers":[{"url":null,"slug":"asymkv-enabling-1-bit-quantization-of-kv","title":"AsymKV: Enabling 1-Bit Quantization of KV Cache with Layer-Wise Asymmetric Quantization Configurations","date":"2024-10-17","arxiv_id":"2410.13212","repositories_listed":0,"syntology":null},{"url":null,"slug":"debiasing-large-vision-language-models-by","title":"Debiasing Large Vision-Language Models by Ablating Protected Attribute Representations","date":"2024-10-17","arxiv_id":"2410.13976","repositories_listed":0,"syntology":null},{"url":null,"slug":"disentangling-likes-and-dislikes-in","title":"Disentangling Likes and Dislikes in Personalized Generative Explainable Recommendation","date":"2024-10-17","arxiv_id":"2410.13248","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-text-generation-in-joint-nlg-nlu","title":"Enhancing Text Generation in Joint NLG/NLU Learning Through Curriculum Learning, Semi-Supervised Training, and Advanced Optimization Techniques","date":"2024-10-17","arxiv_id":"2410.13498","repositories_listed":0,"syntology":null},{"url":null,"slug":"generating-signed-language-instructions-in","title":"Generating Signed Language Instructions in Large-Scale Dialogue Systems","date":"2024-10-17","arxiv_id":"2410.14026","repositories_listed":0,"syntology":null},{"url":null,"slug":"jailbreaking-llm-controlled-robots","title":"Jailbreaking LLM-Controlled Robots","date":"2024-10-17","arxiv_id":"2410.13691","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-language-models-are-easily-confused-a","title":"Large Language Models are Easily Confused: A Quantitative Metric, Security Implications and Typological Analysis","date":"2024-10-17","arxiv_id":"2410.13237","repositories_listed":0,"syntology":null},{"url":null,"slug":"repetition-neurons-how-do-language-models","title":"Repetition Neurons: How Do Language Models Produce Repetitions?","date":"2024-10-17","arxiv_id":"2410.13497","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-automatic-and-cost-efficient-peer-review","title":"An Automatic and Cost-Efficient Peer-Review Framework for Language Generation Evaluation","date":"2024-10-16","arxiv_id":"2410.12265","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-language-models-as-a-tool-for-mining","title":"Large Language Models as a Tool for Mining Object Knowledge","date":"2024-10-16","arxiv_id":"2410.12959","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-a-scale-from-1-to-5-quantifying","title":"On A Scale From 1 to 5: Quantifying Hallucination in Faithfulness Evaluation","date":"2024-10-16","arxiv_id":"2410.12222","repositories_listed":0,"syntology":null},{"url":null,"slug":"promptexp-multi-granularity-prompt","title":"PromptExp: Multi-granularity Prompt Explanation of Large Language Models","date":"2024-10-16","arxiv_id":"2410.13073","repositories_listed":0,"syntology":null},{"url":"/paper/improving-instruction-following-in-language-1","slug":"improving-instruction-following-in-language-1","title":"Improving Instruction-Following in Language Models through Activation Steering","date":"2024-10-15","arxiv_id":"2410.12877","repositories_listed":0,"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/improving-instruction-following-in-language-1#ran","syntology_url":"https://syntology.ai/paper/2410.12877","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.12877"}},"official":null}},{"url":null,"slug":"omcat-omni-context-aware-transformer","title":"OMCAT: Omni Context Aware Transformer","date":"2024-10-15","arxiv_id":"2410.12109","repositories_listed":0,"syntology":null},{"url":null,"slug":"speculative-knowledge-distillation-bridging","title":"Speculative Knowledge Distillation: Bridging the Teacher-Student Gap Through Interleaved Sampling","date":"2024-10-15","arxiv_id":"2410.11325","repositories_listed":0,"syntology":null},{"url":null,"slug":"survey-and-evaluation-of-converging","title":"Survey and Evaluation of Converging Architecture in LLMs based on Footsteps of Operations","date":"2024-10-15","arxiv_id":"2410.11381","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":"improving-the-language-understanding","title":"Improving the Language Understanding Capabilities of Large Language Models Using Reinforcement Learning","date":"2024-10-14","arxiv_id":"2410.11020","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-language-models-are-active-critics-in","title":"Large Language Models Are Active Critics in NLG Evaluation","date":"2024-10-14","arxiv_id":"2410.10724","repositories_listed":0,"syntology":null},{"url":null,"slug":"llm-based-code-switched-text-generation-for","title":"LLM-based Code-Switched Text Generation for Grammatical Error Correction","date":"2024-10-14","arxiv_id":"2410.10349","repositories_listed":0,"syntology":null},{"url":"/paper/mmar-towards-lossless-multi-modal-auto","slug":"mmar-towards-lossless-multi-modal-auto","title":"MMAR: Towards Lossless Multi-Modal Auto-Regressive Probabilistic Modeling","date":"2024-10-14","arxiv_id":"2410.10798","repositories_listed":0,"syntology":null},{"url":null,"slug":"multilingual-controlled-generation-and-gold","title":"Multilingual Controlled Generation And Gold-Standard-Agnostic Evaluation of Code-Mixed Sentences","date":"2024-10-14","arxiv_id":"2410.10580","repositories_listed":0,"syntology":null},{"url":null,"slug":"qe-ebm-using-quality-estimators-as-energy","title":"QE-EBM: Using Quality Estimators as Energy Loss for Machine Translation","date":"2024-10-14","arxiv_id":"2410.10228","repositories_listed":0,"syntology":null},{"url":null,"slug":"bidora-bi-level-optimization-based-weight","title":"BiDoRA: Bi-level Optimization-Based Weight-Decomposed Low-Rank Adaptation","date":"2024-10-13","arxiv_id":"2410.09758","repositories_listed":0,"syntology":null},{"url":null,"slug":"diversity-of-thought-elicits-stronger","title":"Diversity of Thought Elicits Stronger Reasoning Capabilities in Multi-Agent Debate Frameworks","date":"2024-10-10","arxiv_id":"2410.12853","repositories_listed":0,"syntology":null},{"url":null,"slug":"textit-jump-your-steps-optimizing-sampling","title":"$\\textit{Jump Your Steps}$: Optimizing Sampling Schedule of Discrete Diffusion Models","date":"2024-10-10","arxiv_id":"2410.07761","repositories_listed":0,"syntology":null},{"url":null,"slug":"integrating-planning-into-single-turn-long","title":"Integrating Planning into Single-Turn Long-Form Text Generation","date":"2024-10-08","arxiv_id":"2410.06203","repositories_listed":0,"syntology":null},{"url":null,"slug":"parallelspec-parallel-drafter-for-efficient","title":"ParallelSpec: Parallel Drafter for Efficient Speculative Decoding","date":"2024-10-08","arxiv_id":"2410.05589","repositories_listed":0,"syntology":null},{"url":null,"slug":"reviseval-improving-llm-as-a-judge-via","title":"RevisEval: Improving LLM-as-a-Judge via Response-Adapted References","date":"2024-10-07","arxiv_id":"2410.05193","repositories_listed":0,"syntology":null},{"url":null,"slug":"control-large-language-models-via-divide-and","title":"Control Large Language Models via Divide and Conquer","date":"2024-10-06","arxiv_id":"2410.04628","repositories_listed":0,"syntology":null},{"url":null,"slug":"copylens-dynamically-flagging-copyrighted-sub","title":"Inner-Probe: Discovering Copyright-related Data Generation in LLM Architecture","date":"2024-10-06","arxiv_id":"2410.04454","repositories_listed":0,"syntology":null},{"url":null,"slug":"empowering-backbone-models-for-visual-text","title":"Empowering Backbone Models for Visual Text Generation with Input Granularity Control and Glyph-Aware Training","date":"2024-10-06","arxiv_id":"2410.04439","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-the-correctness-of-inference","title":"Evaluating the Correctness of Inference Patterns Used by LLMs for Judgment","date":"2024-10-06","arxiv_id":"2410.09083","repositories_listed":0,"syntology":null},{"url":null,"slug":"suspiciousness-of-adversarial-texts-to-human","title":"Graded Suspiciousness of Adversarial Texts to Human","date":"2024-10-06","arxiv_id":"2410.04377","repositories_listed":0,"syntology":null},{"url":"/paper/pad-personalized-alignment-at-decoding-time","slug":"pad-personalized-alignment-at-decoding-time","title":"PAD: Personalized Alignment of LLMs at Decoding-Time","date":"2024-10-05","arxiv_id":"2410.04070","repositories_listed":0,"syntology":{"n":8,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":6,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/pad-personalized-alignment-at-decoding-time#ran","syntology_url":"https://syntology.ai/paper/2410.04070","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.04070"}},"official":null}},{"url":null,"slug":"crafting-narrative-closures-zero-shot","title":"Crafting Narrative Closures: Zero-Shot Learning with SSM Mamba for Short Story Ending Generation","date":"2024-10-04","arxiv_id":"2410.10848","repositories_listed":0,"syntology":null},{"url":"/paper/decoding-game-on-minimax-optimality-of","slug":"decoding-game-on-minimax-optimality-of","title":"Decoding Game: On Minimax Optimality of Heuristic Text Generation Strategies","date":"2024-10-04","arxiv_id":"2410.03968","repositories_listed":0,"syntology":{"n":14,"n_ran":13,"n_constructed":0,"n_ran_checked":13,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":13,"n_pointer_only":14,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/decoding-game-on-minimax-optimality-of#ran","syntology_url":"https://syntology.ai/paper/2410.03968","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.03968"}},"official":null}},{"url":null,"slug":"explicit-implicit-and-scattered-revisiting","title":"Explicit, Implicit, and Scattered: Revisiting Event Extraction to Capture Complex Arguments","date":"2024-10-04","arxiv_id":"2410.03594","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-prompts-to-guide-large-language-models","title":"Using Prompts to Guide Large Language Models in Imitating a Real Person's Language Style","date":"2024-10-04","arxiv_id":"2410.03848","repositories_listed":0,"syntology":null},{"url":null,"slug":"attention-in-large-language-models-yields","title":"Attention in Large Language Models Yields Efficient Zero-Shot Re-Rankers","date":"2024-10-03","arxiv_id":"2410.02642","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-language-models-take-a-hint-prompting-for-1","title":"Can Language Models Take A Hint? Prompting for Controllable Contextualized Commonsense Inference","date":"2024-10-03","arxiv_id":"2410.02202","repositories_listed":0,"syntology":null},{"url":null,"slug":"reward-rag-enhancing-rag-with-reward-driven","title":"Reward-RAG: Enhancing RAG with Reward Driven Supervision","date":"2024-10-03","arxiv_id":"2410.03780","repositories_listed":0,"syntology":null},{"url":null,"slug":"conformal-generative-modeling-with-improved","title":"Conformal Generative Modeling with Improved Sample Efficiency through Sequential Greedy Filtering","date":"2024-10-02","arxiv_id":"2410.01660","repositories_listed":0,"syntology":null},{"url":null,"slug":"discrete-copula-diffusion","title":"Discrete Copula Diffusion","date":"2024-10-02","arxiv_id":"2410.01949","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-long-range-language-modeling-with","title":"Efficient Length-Generalizable Attention via Causal Retrieval for Long-Context Language Modeling","date":"2024-10-02","arxiv_id":"2410.01651","repositories_listed":0,"syntology":null},{"url":null,"slug":"gadfa-generator-assisted-decision-focused","title":"GADFA: Generator-Assisted Decision-Focused Approach for Opinion Expressing Timing Identification","date":"2024-10-02","arxiv_id":"2410.01169","repositories_listed":0,"syntology":null},{"url":null,"slug":"are-llms-aware-that-some-questions-are-not","title":"Are LLMs Aware that Some Questions are not Open-ended?","date":"2024-10-01","arxiv_id":"2410.00423","repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-ai-application-for-building","title":"Exploring Gen-AI applications in building research and industry: A review","date":"2024-10-01","arxiv_id":"2410.01098","repositories_listed":0,"syntology":null},{"url":null,"slug":"what-is-the-role-of-large-language-models-in","title":"What is the Role of Large Language Models in the Evolution of Astronomy Research?","date":"2024-09-30","arxiv_id":"2409.20252","repositories_listed":0,"syntology":null},{"url":null,"slug":"human-bias-in-the-face-of-ai-the-role-of","title":"Human Bias in the Face of AI: The Role of Human Judgement in AI Generated Text Evaluation","date":"2024-09-29","arxiv_id":"2410.03723","repositories_listed":0,"syntology":null},{"url":null,"slug":"natural-language-generation-for","title":"Natural Language Generation for Visualizations: State of the Art, Challenges and Future Directions","date":"2024-09-29","arxiv_id":"2409.19747","repositories_listed":0,"syntology":null},{"url":null,"slug":"trojvlm-backdoor-attack-against-vision","title":"TrojVLM: Backdoor Attack Against Vision Language Models","date":"2024-09-28","arxiv_id":"2409.19232","repositories_listed":0,"syntology":null},{"url":null,"slug":"experimental-evaluation-of-machine-learning","title":"Experimental Evaluation of Machine Learning Models for Goal-oriented Customer Service Chatbot with Pipeline Architecture","date":"2024-09-27","arxiv_id":"2409.18568","repositories_listed":0,"syntology":null},{"url":null,"slug":"hit-the-sweet-spot-span-level-ensemble-for","title":"Hit the Sweet Spot! Span-Level Ensemble for Large Language Models","date":"2024-09-27","arxiv_id":"2409.18583","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-power-of-decision-trees-in-auto","title":"On the Power of Decision Trees in Auto-Regressive Language Modeling","date":"2024-09-27","arxiv_id":"2409.19150","repositories_listed":0,"syntology":null},{"url":null,"slug":"egolm-multi-modal-language-model-of","title":"EgoLM: Multi-Modal Language Model of Egocentric Motions","date":"2024-09-26","arxiv_id":"2409.18127","repositories_listed":0,"syntology":null},{"url":null,"slug":"embodied-rag-general-non-parametric-embodied","title":"Embodied-RAG: General Non-parametric Embodied Memory for Retrieval and Generation","date":"2024-09-26","arxiv_id":"2409.18313","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluation-of-large-language-models-for-3","title":"Evaluation of Large Language Models for Summarization Tasks in the Medical Domain: A Narrative Review","date":"2024-09-26","arxiv_id":"2409.18170","repositories_listed":0,"syntology":null},{"url":null,"slug":"joytype-a-robust-design-for-multilingual","title":"JoyType: A Robust Design for Multilingual Visual Text Creation","date":"2024-09-26","arxiv_id":"2409.17524","repositories_listed":0,"syntology":null},{"url":null,"slug":"trustworthy-ai-securing-sensitive-data-in","title":"Trustworthy AI: Securing Sensitive Data in Large Language Models","date":"2024-09-26","arxiv_id":"2409.18222","repositories_listed":0,"syntology":null},{"url":null,"slug":"accumulator-aware-post-training-quantization","title":"Accumulator-Aware Post-Training Quantization","date":"2024-09-25","arxiv_id":"2409.17092","repositories_listed":0,"syntology":null},{"url":null,"slug":"application-of-ai-based-models-for-online","title":"Application of AI-based Models for Online Fraud Detection and Analysis","date":"2024-09-25","arxiv_id":"2409.19022","repositories_listed":0,"syntology":null},{"url":null,"slug":"axcel-automated-explainable-consistency","title":"AXCEL: Automated eXplainable Consistency Evaluation using LLMs","date":"2024-09-25","arxiv_id":"2409.16984","repositories_listed":0,"syntology":null},{"url":null,"slug":"overview-of-the-first-shared-task-on-clinical","title":"Overview of the First Shared Task on Clinical Text Generation: RRG24 and \"Discharge Me!\"","date":"2024-09-25","arxiv_id":"2409.16603","repositories_listed":0,"syntology":null},{"url":null,"slug":"probing-omissions-and-distortions-in","title":"Probing Omissions and Distortions in Transformer-based RDF-to-Text Models","date":"2024-09-25","arxiv_id":"2409.16707","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comprehensive-survey-of-bias-in-llms","title":"A Comprehensive Survey of Bias in LLMs: Current Landscape and Future Directions","date":"2024-09-24","arxiv_id":"2409.16430","repositories_listed":0,"syntology":null},{"url":null,"slug":"boosting-code-switching-asr-with-mixture-of","title":"Boosting Code-Switching ASR with Mixture of Experts Enhanced Speech-Conditioned LLM","date":"2024-09-24","arxiv_id":"2409.15905","repositories_listed":0,"syntology":null},{"url":null,"slug":"expert-level-vision-language-foundation-model","title":"Expert-level vision-language foundation model for real-world radiology and comprehensive evaluation","date":"2024-09-24","arxiv_id":"2409.16183","repositories_listed":0,"syntology":null},{"url":null,"slug":"finetuning-llms-for-comparative-assessment","title":"Finetuning LLMs for Comparative Assessment Tasks","date":"2024-09-24","arxiv_id":"2409.15979","repositories_listed":0,"syntology":null},{"url":null,"slug":"qualitative-insights-tool-qualit-llm-enhanced","title":"Qualitative Insights Tool (QualIT): LLM Enhanced Topic Modeling","date":"2024-09-24","arxiv_id":"2409.15626","repositories_listed":0,"syntology":null},{"url":null,"slug":"advancing-video-quality-assessment-for-aigc","title":"Advancing Video Quality Assessment for AIGC","date":"2024-09-23","arxiv_id":"2409.14888","repositories_listed":0,"syntology":null},{"url":null,"slug":"enabling-resource-efficient-on-device-fine","title":"Enabling Efficient On-Device Fine-Tuning of LLMs Using Only Inference Engines","date":"2024-09-23","arxiv_id":"2409.15520","repositories_listed":0,"syntology":null},{"url":null,"slug":"backtracking-improves-generation-safety","title":"Backtracking Improves Generation Safety","date":"2024-09-22","arxiv_id":"2409.14586","repositories_listed":0,"syntology":null},{"url":"/paper/loop-residual-neural-networks-for-iterative","slug":"loop-residual-neural-networks-for-iterative","title":"Loop Neural Networks for Parameter Sharing","date":"2024-09-21","arxiv_id":"2409.14199","repositories_listed":0,"syntology":null},{"url":null,"slug":"joyhallo-digital-human-model-for-mandarin","title":"JoyHallo: Digital human model for Mandarin","date":"2024-09-20","arxiv_id":"2409.13268","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-knowledge-graphs-and-llms-to","title":"Leveraging Knowledge Graphs and LLMs to Support and Monitor Legislative Systems","date":"2024-09-20","arxiv_id":"2409.13252","repositories_listed":0,"syntology":null},{"url":null,"slug":"since-lawyers-are-males-examining-implicit","title":"'Since Lawyers are Males..': Examining Implicit Gender Bias in Hindi Language Generation by LLMs","date":"2024-09-20","arxiv_id":"2409.13484","repositories_listed":0,"syntology":null},{"url":null,"slug":"unlocking-memorization-in-large-language","title":"Unlocking Memorization in Large Language Models with Dynamic Soft Prompting","date":"2024-09-20","arxiv_id":"2409.13853","repositories_listed":0,"syntology":null},{"url":null,"slug":"defending-against-reverse-preference-attacks","title":"Mitigating Unsafe Feedback with Learning Constraints","date":"2024-09-19","arxiv_id":"2409.12914","repositories_listed":0,"syntology":null},{"url":null,"slug":"llms-can-check-their-own-results-to-mitigate","title":"LLMs Can Check Their Own Results to Mitigate Hallucinations in Traffic Understanding Tasks","date":"2024-09-19","arxiv_id":"2409.12580","repositories_listed":0,"syntology":null},{"url":null,"slug":"abhinaw-a-method-for-automatic-evaluation-of","title":"ABHINAW: A method for Automatic Evaluation of Typography within AI-Generated Images","date":"2024-09-18","arxiv_id":"2409.11874","repositories_listed":0,"syntology":null},{"url":null,"slug":"zero-resource-hallucination-detection-for","title":"Zero-resource Hallucination Detection for Text Generation via Graph-based Contextual Knowledge Triples Modeling","date":"2024-09-17","arxiv_id":"2409.11283","repositories_listed":0,"syntology":null},{"url":null,"slug":"mgsa-multi-granularity-graph-structure","title":"MGSA: Multi-Granularity Graph Structure Attention for Knowledge Graph-to-Text Generation","date":"2024-09-16","arxiv_id":"2409.10294","repositories_listed":0,"syntology":null},{"url":null,"slug":"reflectdiffu-reflect-between-emotion-intent","title":"ReflectDiffu:Reflect between Emotion-intent Contagion and Mimicry for Empathetic Response Generation via a RL-Diffusion Framework","date":"2024-09-16","arxiv_id":"2409.10289","repositories_listed":0,"syntology":null},{"url":null,"slug":"nevlp-noise-robust-framework-for-efficient","title":"NEVLP: Noise-Robust Framework for Efficient Vision-Language Pre-training","date":"2024-09-15","arxiv_id":"2409.09582","repositories_listed":0,"syntology":null},{"url":null,"slug":"personamark-personalized-llm-watermarking-for","title":"PersonaMark: Personalized LLM watermarking for model protection and user attribution","date":"2024-09-15","arxiv_id":"2409.09739","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-diverse-and-efficient-audio","title":"Towards Diverse and Efficient Audio Captioning via Diffusion Models","date":"2024-09-14","arxiv_id":"2409.09401","repositories_listed":0,"syntology":null},{"url":"/paper/reranking-laws-for-language-generation-a","slug":"reranking-laws-for-language-generation-a","title":"Reranking Laws for Language Generation: A Communication-Theoretic Perspective","date":"2024-09-11","arxiv_id":"2409.07131","repositories_listed":0,"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/reranking-laws-for-language-generation-a#ran","syntology_url":"https://syntology.ai/paper/2409.07131","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.07131"}},"official":null}},{"url":null,"slug":"table-to-text-generation-with-pretrained","title":"Table-to-Text Generation with Pretrained Diffusion Models","date":"2024-09-10","arxiv_id":"2409.13739","repositories_listed":0,"syntology":null},{"url":null,"slug":"topochat-enhancing-topological-materials","title":"Enhancing Large Language Models with Domain-Specific Knowledge: The Case in Topological Materials","date":"2024-09-10","arxiv_id":"2409.13732","repositories_listed":0,"syntology":null},{"url":null,"slug":"elsevier-arena-human-evaluation-of-chemistry","title":"Elsevier Arena: Human Evaluation of Chemistry/Biology/Health Foundational Large Language Models","date":"2024-09-09","arxiv_id":"2409.05486","repositories_listed":0,"syntology":null},{"url":null,"slug":"identity-related-speech-suppression-in","title":"Identity-related Speech Suppression in Generative AI Content Moderation","date":"2024-09-09","arxiv_id":"2409.13725","repositories_listed":0,"syntology":null},{"url":null,"slug":"llms-will-always-hallucinate-and-we-need-to","title":"LLMs Will Always Hallucinate, and We Need to Live With This","date":"2024-09-09","arxiv_id":"2409.05746","repositories_listed":0,"syntology":null},{"url":null,"slug":"loca-logit-calibration-for-knowledge","title":"LoCa: Logit Calibration for Knowledge Distillation","date":"2024-09-07","arxiv_id":"2409.04778","repositories_listed":0,"syntology":null},{"url":null,"slug":"chain-of-translation-prompting-cotr-a-novel","title":"Chain-of-Translation Prompting (CoTR): A Novel Prompting Technique for Low Resource Languages","date":"2024-09-06","arxiv_id":"2409.04512","repositories_listed":0,"syntology":null},{"url":null,"slug":"bypassing-darcy-defense-indistinguishable","title":"Bypassing DARCY Defense: Indistinguishable Universal Adversarial Triggers","date":"2024-09-05","arxiv_id":"2409.03183","repositories_listed":0,"syntology":null},{"url":null,"slug":"clue-concept-level-uncertainty-estimation-for","title":"CLUE: Concept-Level Uncertainty Estimation for Large Language Models","date":"2024-09-04","arxiv_id":"2409.03021","repositories_listed":0,"syntology":null},{"url":null,"slug":"deconfounded-causality-aware-parameter","title":"Deconfounded Causality-aware Parameter-Efficient Fine-Tuning for Problem-Solving Improvement of LLMs","date":"2024-09-04","arxiv_id":"2409.02686","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-privacy-savvy-are-large-language-models-a","title":"How Privacy-Savvy Are Large Language Models? A Case Study on Compliance and Privacy Technical Review","date":"2024-09-04","arxiv_id":"2409.02375","repositories_listed":0,"syntology":null},{"url":null,"slug":"llm-assisted-visual-analytics-opportunities","title":"LLM-Assisted Visual Analytics: Opportunities and Challenges","date":"2024-09-04","arxiv_id":"2409.02691","repositories_listed":0,"syntology":null}],"record_sha256":"afb1627b1731e3bb500706e17474359e25eb8b85d5fd694fb005698cf0798575","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}