{"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/cosine-annealing/papers/13","list_of":"/method/cosine-annealing","method":"Cosine Annealing","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":13,"pages_in_order":40,"rows_per_page":100,"rows":[1201,1300],"of":3965,"counts":{"archive_papers_tagged":3965,"with_a_code_link":1734,"where_syntology_ran_a_sample":627,"not_listed_spam_title":0,"listed":3965,"listed_where_code_ran":627,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":513,"every_run_a_failure_of_syntologys_instrument":114,"listed_with_a_run_with_no_instrument_failure":513,"listed_every_run_a_failure_of_syntologys_instrument":114,"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/cosine-annealing","prev":"/method/cosine-annealing/papers/12","next":"/method/cosine-annealing/papers/14","papers":[{"paper":"/paper/hw-gpt-bench-hardware-aware-architecture","slug":"hw-gpt-bench-hardware-aware-architecture","title":"HW-GPT-Bench: Hardware-Aware Architecture Benchmark for Language Models","date":"2024-05-16","arxiv_id":"2405.10299","n_code_links":2,"syntology":null},{"paper":null,"slug":"optimization-techniques-for-sentiment","title":"Optimization Techniques for Sentiment Analysis Based on LLM (GPT-3)","date":"2024-05-16","arxiv_id":"2405.09770","n_code_links":0,"syntology":null},{"paper":null,"slug":"matching-domain-experts-by-training-from","title":"Matching domain experts by training from scratch on domain knowledge","date":"2024-05-15","arxiv_id":"2405.09395","n_code_links":0,"syntology":null},{"paper":null,"slug":"beyond-scaling-laws-understanding-transformer","title":"Beyond Scaling Laws: Understanding Transformer Performance with Associative Memory","date":"2024-05-14","arxiv_id":"2405.08707","n_code_links":0,"syntology":null},{"paper":null,"slug":"challenges-in-deploying-long-context","title":"Challenges in Deploying Long-Context Transformers: A Theoretical Peak Performance Analysis","date":"2024-05-14","arxiv_id":"2405.08944","n_code_links":0,"syntology":null},{"paper":"/paper/gpt-3-5-for-grammatical-error-correction","slug":"gpt-3-5-for-grammatical-error-correction","title":"GPT-3.5 for Grammatical Error Correction","date":"2024-05-14","arxiv_id":"2405.08469","n_code_links":0,"syntology":null},{"paper":null,"slug":"refinement-of-an-epilepsy-dictionary-through","title":"Refinement of an Epilepsy Dictionary through Human Annotation of Health-related posts on Instagram","date":"2024-05-14","arxiv_id":"2405.08784","n_code_links":0,"syntology":null},{"paper":null,"slug":"towards-principled-evaluations-of-sparse","title":"Towards Principled Evaluations of Sparse Autoencoders for Interpretability and Control","date":"2024-05-14","arxiv_id":"2405.08366","n_code_links":0,"syntology":null},{"paper":null,"slug":"when-large-language-models-meet-optical","title":"When Large Language Models Meet Optical Networks: Paving the Way for Automation","date":"2024-05-14","arxiv_id":"2405.17441","n_code_links":0,"syntology":null},{"paper":"/paper/can-language-models-explain-their-own","slug":"can-language-models-explain-their-own","title":"Can Language Models Explain Their Own Classification Behavior?","date":"2024-05-13","arxiv_id":"2405.07436","n_code_links":1,"syntology":null},{"paper":"/paper/coding-historical-causes-of-death-data-with","slug":"coding-historical-causes-of-death-data-with","title":"Coding historical causes of death data with Large Language Models","date":"2024-05-13","arxiv_id":"2405.07560","n_code_links":1,"syntology":null},{"paper":"/paper/freeva-offline-mllm-as-training-free-video","slug":"freeva-offline-mllm-as-training-free-video","title":"FreeVA: Offline MLLM as Training-Free Video Assistant","date":"2024-05-13","arxiv_id":"2405.07798","n_code_links":1,"syntology":{"ran":8,"of":9,"n_ran_checked":4,"n_instrument":4,"unverified":1,"pointer_only":1,"phrase":"8 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; 4 where Syntology's instrument failed) · 1 unverified","official":{"repos":["whwu95/freeva"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/macbehaviour-an-r-package-for-behavioural","slug":"macbehaviour-an-r-package-for-behavioural","title":"MacBehaviour: An R package for behavioural experimentation on large language models","date":"2024-05-13","arxiv_id":"2405.07495","n_code_links":1,"syntology":null},{"paper":null,"slug":"many-shot-regurgitation-msr-prompting","title":"Many-Shot Regurgitation (MSR) Prompting","date":"2024-05-13","arxiv_id":"2405.08134","n_code_links":0,"syntology":null},{"paper":null,"slug":"open-vocabulary-auditory-neural-decoding","title":"Open-vocabulary Auditory Neural Decoding Using fMRI-prompted LLM","date":"2024-05-13","arxiv_id":"2405.07840","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-reward-for-robot-skills-using-large","title":"Learning Reward for Robot Skills Using Large Language Models via Self-Alignment","date":"2024-05-12","arxiv_id":"2405.07162","n_code_links":0,"syntology":null},{"paper":"/paper/limited-ability-of-llms-to-simulate-human","slug":"limited-ability-of-llms-to-simulate-human","title":"Limited Ability of LLMs to Simulate Human Psychological Behaviours: a Psychometric Analysis","date":"2024-05-12","arxiv_id":"2405.07248","n_code_links":1,"syntology":{"ran":1,"of":2,"n_ran_checked":0,"n_instrument":1,"unverified":1,"pointer_only":2,"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) · 1 unverified","official":{"repos":["nikbpetrov/llms-simulate-humans"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/quite-good-but-not-enough-nationality-bias-in","slug":"quite-good-but-not-enough-nationality-bias-in","title":"Quite Good, but Not Enough: Nationality Bias in Large Language Models -- A Case Study of ChatGPT","date":"2024-05-11","arxiv_id":"2405.06996","n_code_links":1,"syntology":null},{"paper":null,"slug":"retrieval-enhanced-zero-shot-video-captioning","title":"RETTA: Retrieval-Enhanced Test-Time Adaptation for Zero-Shot Video Captioning","date":"2024-05-11","arxiv_id":"2405.07046","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-assessment-of-model-on-model-deception","title":"An Assessment of Model-On-Model Deception","date":"2024-05-10","arxiv_id":"2405.12999","n_code_links":0,"syntology":null},{"paper":null,"slug":"chatgptest-opportunities-and-cautionary-tales","title":"ChatGPTest: opportunities and cautionary tales of utilizing AI for questionnaire pretesting","date":"2024-05-10","arxiv_id":"2405.06329","n_code_links":0,"syntology":null},{"paper":"/paper/a-mixture-of-experts-approach-to-3d-human","slug":"a-mixture-of-experts-approach-to-3d-human","title":"A Mixture of Experts Approach to 3D Human Motion Prediction","date":"2024-05-09","arxiv_id":"2405.06088","n_code_links":1,"syntology":null},{"paper":"/paper/audio-visual-speech-recognition-based-on","slug":"audio-visual-speech-recognition-based-on","title":"Audio-Visual Speech Recognition based on Regulated Transformer and Spatio-Temporal Fusion Strategy for Driver Assistive Systems","date":"2024-05-09","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"can-large-language-models-understand-uncommon","title":"Can large language models understand uncommon meanings of common words?","date":"2024-05-09","arxiv_id":"2405.05741","n_code_links":0,"syntology":null},{"paper":null,"slug":"digital-diagnostics-the-potential-of-large","title":"Digital Diagnostics: The Potential Of Large Language Models In Recognizing Symptoms Of Common Illnesses","date":"2024-05-09","arxiv_id":"2405.06712","n_code_links":0,"syntology":null},{"paper":null,"slug":"iris-an-ai-driven-virtual-tutor-for-computer","title":"Iris: An AI-Driven Virtual Tutor For Computer Science Education","date":"2024-05-09","arxiv_id":"2405.08008","n_code_links":0,"syntology":null},{"paper":null,"slug":"people-cannot-distinguish-gpt-4-from-a-human","title":"People cannot distinguish GPT-4 from a human in a Turing test","date":"2024-05-09","arxiv_id":"2405.08007","n_code_links":0,"syntology":null},{"paper":null,"slug":"reddit-impacts-a-named-entity-recognition","title":"Reddit-Impacts: A Named Entity Recognition Dataset for Analyzing Clinical and Social Effects of Substance Use Derived from Social Media","date":"2024-05-09","arxiv_id":"2405.06145","n_code_links":0,"syntology":null},{"paper":null,"slug":"unveiling-the-competitive-dynamics-a","title":"Unveiling the Competitive Dynamics: A Comparative Evaluation of American and Chinese LLMs","date":"2024-05-09","arxiv_id":"2405.06713","n_code_links":0,"syntology":null},{"paper":null,"slug":"air-gap-protecting-privacy-conscious","title":"AirGapAgent: Protecting Privacy-Conscious Conversational Agents","date":"2024-05-08","arxiv_id":"2405.05175","n_code_links":0,"syntology":null},{"paper":"/paper/care-sd-classifier-based-analysis-for","slug":"care-sd-classifier-based-analysis-for","title":"CARE-SD: Classifier-based analysis for recognizing and eliminating stigmatizing and doubt marker labels in electronic health records: model development and validation","date":"2024-05-08","arxiv_id":"2405.05204","n_code_links":1,"syntology":null},{"paper":null,"slug":"llms-can-patch-up-missing-relevance-judgments","title":"LLMs Can Patch Up Missing Relevance Judgments in Evaluation","date":"2024-05-08","arxiv_id":"2405.04727","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-llm-tool-compiler-for-fused-parallel","title":"An LLM-Tool Compiler for Fused Parallel Function Calling","date":"2024-05-07","arxiv_id":"2405.17438","n_code_links":0,"syntology":null},{"paper":null,"slug":"evaluating-text-summaries-generated-by-large","title":"Evaluating Text Summaries Generated by Large Language Models Using OpenAI's GPT","date":"2024-05-07","arxiv_id":"2405.04053","n_code_links":0,"syntology":null},{"paper":null,"slug":"gpt-enabled-cybersecurity-training-a-tailored","title":"GPT-Enabled Cybersecurity Training: A Tailored Approach for Effective Awareness","date":"2024-05-07","arxiv_id":"2405.04138","n_code_links":0,"syntology":null},{"paper":"/paper/how-does-gpt-2-predict-acronyms-extracting","slug":"how-does-gpt-2-predict-acronyms-extracting","title":"How does GPT-2 Predict Acronyms? Extracting and Understanding a Circuit via Mechanistic Interpretability","date":"2024-05-07","arxiv_id":"2405.04156","n_code_links":1,"syntology":{"ran":3,"of":4,"n_ran_checked":1,"n_instrument":2,"unverified":1,"pointer_only":4,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","official":{"repos":["jgcarrasco/acronyms_paper"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"long-context-alignment-with-short","title":"Long Context Alignment with Short Instructions and Synthesized Positions","date":"2024-05-07","arxiv_id":"2405.03939","n_code_links":0,"syntology":null},{"paper":null,"slug":"sutra-scalable-multilingual-language-model","title":"SUTRA: Scalable Multilingual Language Model Architecture","date":"2024-05-07","arxiv_id":"2405.06694","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-silicone-ceiling-auditing-gpt-s-race-and","title":"The Silicon Ceiling: Auditing GPT's Race and Gender Biases in Hiring","date":"2024-05-07","arxiv_id":"2405.04412","n_code_links":0,"syntology":null},{"paper":"/paper/anchored-answers-unravelling-positional-bias","slug":"anchored-answers-unravelling-positional-bias","title":"Anchored Answers: Unravelling Positional Bias in GPT-2's Multiple-Choice Questions","date":"2024-05-06","arxiv_id":"2405.03205","n_code_links":1,"syntology":null},{"paper":"/paper/hire-me-or-not-examining-language-model-s","slug":"hire-me-or-not-examining-language-model-s","title":"Hire Me or Not? Examining Language Model's Behavior with Occupation Attributes","date":"2024-05-06","arxiv_id":"2405.06687","n_code_links":1,"syntology":{"ran":10,"of":13,"n_ran_checked":10,"n_instrument":0,"unverified":3,"pointer_only":13,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","official":{"repos":["daminz97/multi-step_gsv"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":"/paper/large-language-models-reveal-information","slug":"large-language-models-reveal-information","title":"Large Language Models Reveal Information Operation Goals, Tactics, and Narrative Frames","date":"2024-05-06","arxiv_id":"2405.03688","n_code_links":1,"syntology":null},{"paper":null,"slug":"can-large-language-models-make-the-grade-an","title":"Can Large Language Models Make the Grade? An Empirical Study Evaluating LLMs Ability to Mark Short Answer Questions in K-12 Education","date":"2024-05-05","arxiv_id":"2405.02985","n_code_links":0,"syntology":null},{"paper":"/paper/huixiangdou-cr-coreference-resolution-in","slug":"huixiangdou-cr-coreference-resolution-in","title":"Labeling supervised fine-tuning data with the scaling law","date":"2024-05-05","arxiv_id":"2405.02817","n_code_links":2,"syntology":null},{"paper":null,"slug":"overconfidence-is-key-verbalized-uncertainty","title":"Overconfidence is Key: Verbalized Uncertainty Evaluation in Large Language and Vision-Language Models","date":"2024-05-05","arxiv_id":"2405.02917","n_code_links":0,"syntology":null},{"paper":"/paper/unraveling-the-dominance-of-large-language","slug":"unraveling-the-dominance-of-large-language","title":"Unraveling the Dominance of Large Language Models Over Transformer Models for Bangla Natural Language Inference: A Comprehensive Study","date":"2024-05-05","arxiv_id":"2405.02937","n_code_links":1,"syntology":null},{"paper":null,"slug":"evaluating-large-language-models-for-2","title":"Evaluating Large Language Models for Structured Science Summarization in the Open Research Knowledge Graph","date":"2024-05-03","arxiv_id":"2405.02105","n_code_links":0,"syntology":null},{"paper":null,"slug":"exploring-combinatorial-problem-solving-with","title":"Exploring Combinatorial Problem Solving with Large Language Models: A Case Study on the Travelling Salesman Problem Using GPT-3.5 Turbo","date":"2024-05-03","arxiv_id":"2405.01997","n_code_links":0,"syntology":null},{"paper":null,"slug":"reasons-a-benchmark-for-retrieval-and","title":"Attribution in Scientific Literature: New Benchmark and Methods","date":"2024-05-03","arxiv_id":"2405.02228","n_code_links":0,"syntology":null},{"paper":"/paper/a-survey-on-large-language-models-for-3","slug":"a-survey-on-large-language-models-for-3","title":"A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law","date":"2024-05-02","arxiv_id":"2405.01769","n_code_links":1,"syntology":null},{"paper":null,"slug":"bayesian-optimization-with-llm-based","title":"Bayesian Optimization with LLM-Based Acquisition Functions for Natural Language Preference Elicitation","date":"2024-05-02","arxiv_id":"2405.00981","n_code_links":0,"syntology":null},{"paper":null,"slug":"early-transformers-a-study-on-efficient","title":"Early Transformers: A study on Efficient Training of Transformer Models through Early-Bird Lottery Tickets","date":"2024-05-02","arxiv_id":"2405.02353","n_code_links":0,"syntology":null},{"paper":"/paper/investigating-wit-creativity-and","slug":"investigating-wit-creativity-and","title":"Investigating Wit, Creativity, and Detectability of Large Language Models in Domain-Specific Writing Style Adaptation of Reddit's Showerthoughts","date":"2024-05-02","arxiv_id":"2405.01660","n_code_links":1,"syntology":null},{"paper":null,"slug":"the-effectiveness-of-llms-as-annotators-a","title":"The Effectiveness of LLMs as Annotators: A Comparative Overview and Empirical Analysis of Direct Representation","date":"2024-05-02","arxiv_id":"2405.01299","n_code_links":0,"syntology":null},{"paper":null,"slug":"courseassist-pedagogically-appropriate","title":"CourseAssist: Pedagogically Appropriate AI Tutor for Computer Science Education","date":"2024-05-01","arxiv_id":"2407.10246","n_code_links":0,"syntology":null},{"paper":null,"slug":"how-can-i-improve-using-gpt-to-highlight-the","title":"How Can I Improve? Using GPT to Highlight the Desired and Undesired Parts of Open-ended Responses","date":"2024-05-01","arxiv_id":"2405.00291","n_code_links":0,"syntology":null},{"paper":"/paper/better-faster-large-language-models-via-multi","slug":"better-faster-large-language-models-via-multi","title":"Better & Faster Large Language Models via Multi-token Prediction","date":"2024-04-30","arxiv_id":"2404.19737","n_code_links":1,"syntology":null},{"paper":null,"slug":"can-large-language-models-put-2-and-2","title":"Can Large Language Models put 2 and 2 together? Probing for Entailed Arithmetical Relationships","date":"2024-04-30","arxiv_id":"2404.19432","n_code_links":0,"syntology":null},{"paper":"/paper/do-large-language-models-understand","slug":"do-large-language-models-understand","title":"Do Large Language Models Understand Conversational Implicature -- A case study with a chinese sitcom","date":"2024-04-30","arxiv_id":"2404.19509","n_code_links":1,"syntology":{"ran":2,"of":6,"n_ran_checked":2,"n_instrument":0,"unverified":4,"pointer_only":6,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 1 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","official":{"repos":["sjtu-compling/llm-pragmatics"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":"/paper/graphical-reasoning-llm-based-semi-open","slug":"graphical-reasoning-llm-based-semi-open","title":"Graphical Reasoning: LLM-based Semi-Open Relation Extraction","date":"2024-04-30","arxiv_id":"2405.00216","n_code_links":1,"syntology":null},{"paper":null,"slug":"pangea-procedural-artificial-narrative-using","title":"PANGeA: Procedural Artificial Narrative using Generative AI for Turn-Based Video Games","date":"2024-04-30","arxiv_id":"2404.19721","n_code_links":0,"syntology":null},{"paper":null,"slug":"transferring-troubles-cross-lingual","title":"TuBA: Cross-Lingual Transferability of Backdoor Attacks in LLMs with Instruction Tuning","date":"2024-04-30","arxiv_id":"2404.19597","n_code_links":0,"syntology":null},{"paper":"/paper/do-neutral-prompts-produce-insecure-code","slug":"do-neutral-prompts-produce-insecure-code","title":"How secure is AI-generated Code: A Large-Scale Comparison of Large Language Models","date":"2024-04-29","arxiv_id":"2404.18353","n_code_links":1,"syntology":null},{"paper":null,"slug":"evaluating-and-mitigating-linguistic","title":"Evaluating and Mitigating Linguistic Discrimination in Large Language Models","date":"2024-04-29","arxiv_id":"2404.18534","n_code_links":0,"syntology":null},{"paper":null,"slug":"gpt-4-passes-most-of-the-297-written-polish","title":"GPT-4 passes most of the 297 written Polish Board Certification Examinations","date":"2024-04-29","arxiv_id":"2405.01589","n_code_links":0,"syntology":null},{"paper":"/paper/pecc-problem-extraction-and-coding-challenges","slug":"pecc-problem-extraction-and-coding-challenges","title":"PECC: Problem Extraction and Coding Challenges","date":"2024-04-29","arxiv_id":"2404.18766","n_code_links":1,"syntology":null},{"paper":null,"slug":"time-machine-gpt","title":"Time Machine GPT","date":"2024-04-29","arxiv_id":"2404.18543","n_code_links":0,"syntology":null},{"paper":null,"slug":"can-perplexity-predict-fine-tuning","title":"Can Perplexity Predict Fine-Tuning Performance? An Investigation of Tokenization Effects on Sequential Language Models for Nepali","date":"2024-04-28","arxiv_id":"2404.18071","n_code_links":0,"syntology":null},{"paper":null,"slug":"detection-of-conspiracy-theories-beyond","title":"Detection of Conspiracy Theories Beyond Keyword Bias in German-Language Telegram Using Large Language Models","date":"2024-04-27","arxiv_id":"2404.17985","n_code_links":0,"syntology":null},{"paper":null,"slug":"evaluation-of-few-shot-learning-for","title":"Evaluation of Few-Shot Learning for Classification Tasks in the Polish Language","date":"2024-04-27","arxiv_id":"2404.17832","n_code_links":0,"syntology":null},{"paper":null,"slug":"gpt-for-games-a-scoping-review-2020-2023","title":"GPT for Games: A Scoping Review (2020-2023)","date":"2024-04-27","arxiv_id":"2404.17794","n_code_links":0,"syntology":null},{"paper":null,"slug":"mrscore-evaluating-radiology-report","title":"MRScore: Evaluating Radiology Report Generation with LLM-based Reward System","date":"2024-04-27","arxiv_id":"2404.17778","n_code_links":0,"syntology":null},{"paper":"/paper/automated-data-visualization-from-natural","slug":"automated-data-visualization-from-natural","title":"Automated Data Visualization from Natural Language via Large Language Models: An Exploratory Study","date":"2024-04-26","arxiv_id":"2404.17136","n_code_links":1,"syntology":null},{"paper":null,"slug":"chatgpt-is-here-to-help-not-to-replace","title":"\"ChatGPT Is Here to Help, Not to Replace Anybody\" -- An Evaluation of Students' Opinions On Integrating ChatGPT In CS Courses","date":"2024-04-26","arxiv_id":"2404.17443","n_code_links":0,"syntology":null},{"paper":null,"slug":"enhancing-legal-compliance-and-regulation","title":"Enhancing Legal Compliance and Regulation Analysis with Large Language Models","date":"2024-04-26","arxiv_id":"2404.17522","n_code_links":0,"syntology":null},{"paper":null,"slug":"prompting-towards-alleviating-code-switched","title":"Prompting Towards Alleviating Code-Switched Data Scarcity in Under-Resourced Languages with GPT as a Pivot","date":"2024-04-26","arxiv_id":"2404.17216","n_code_links":0,"syntology":null},{"paper":null,"slug":"quantifying-memorization-of-domain-specific","title":"Quantifying Memorization and Detecting Training Data of Pre-trained Language Models using Japanese Newspaper","date":"2024-04-26","arxiv_id":"2404.17143","n_code_links":0,"syntology":null},{"paper":"/paper/indicgenbench-a-multilingual-benchmark-to","slug":"indicgenbench-a-multilingual-benchmark-to","title":"IndicGenBench: A Multilingual Benchmark to Evaluate Generation Capabilities of LLMs on Indic Languages","date":"2024-04-25","arxiv_id":"2404.16816","n_code_links":1,"syntology":null},{"paper":null,"slug":"influence-of-solution-efficiency-and-valence","title":"Influence of Solution Efficiency and Valence of Instruction on Additive and Subtractive Solution Strategies in Humans and GPT-4","date":"2024-04-25","arxiv_id":"2404.16692","n_code_links":0,"syntology":null},{"paper":"/paper/worldvaluesbench-a-large-scale-benchmark","slug":"worldvaluesbench-a-large-scale-benchmark","title":"WorldValuesBench: A Large-Scale Benchmark Dataset for Multi-Cultural Value Awareness of Language Models","date":"2024-04-25","arxiv_id":"2404.16308","n_code_links":1,"syntology":{"ran":3,"of":5,"n_ran_checked":3,"n_instrument":0,"unverified":2,"pointer_only":5,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["demon702/worldvaluesbench"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"a-comprehensive-survey-on-evaluating-large","title":"A Comprehensive Survey on Evaluating Large Language Model Applications in the Medical Industry","date":"2024-04-24","arxiv_id":"2404.15777","n_code_links":0,"syntology":null},{"paper":null,"slug":"automated-creation-of-source-code-variants-of","title":"Automated Creation of Source Code Variants of a Cryptographic Hash Function Implementation Using Generative Pre-Trained Transformer Models","date":"2024-04-24","arxiv_id":"2404.15681","n_code_links":0,"syntology":null},{"paper":null,"slug":"bert-vs-gpt-for-financial-engineering","title":"BERT vs GPT for financial engineering","date":"2024-04-24","arxiv_id":"2405.12990","n_code_links":0,"syntology":null},{"paper":"/paper/learning-long-form-video-prior-via-generative","slug":"learning-long-form-video-prior-via-generative","title":"Learning Long-form Video Prior via Generative Pre-Training","date":"2024-04-24","arxiv_id":"2404.15909","n_code_links":1,"syntology":null},{"paper":null,"slug":"the-promise-and-challenges-of-using-llms-to","title":"The Promise and Challenges of Using LLMs to Accelerate the Screening Process of Systematic Reviews","date":"2024-04-24","arxiv_id":"2404.15667","n_code_links":0,"syntology":null},{"paper":null,"slug":"automated-multi-language-to-english-machine","title":"Automated Multi-Language to English Machine Translation Using Generative Pre-Trained Transformers","date":"2024-04-23","arxiv_id":"2404.14680","n_code_links":0,"syntology":null},{"paper":null,"slug":"large-language-models-spot-phishing-emails","title":"Evaluating the Efficacy of Large Language Models in Identifying Phishing Attempts","date":"2024-04-23","arxiv_id":"2404.15485","n_code_links":0,"syntology":null},{"paper":null,"slug":"prism-patient-records-interpretation-for","title":"PRISM: Patient Records Interpretation for Semantic Clinical Trial Matching using Large Language Models","date":"2024-04-23","arxiv_id":"2404.15549","n_code_links":0,"syntology":null},{"paper":null,"slug":"science-written-by-generative-ai-is-perceived","title":"From Complexity to Clarity: How AI Enhances Perceptions of Scientists and the Public's Understanding of Science","date":"2024-04-23","arxiv_id":"2405.00706","n_code_links":0,"syntology":null},{"paper":null,"slug":"talk-too-much-poisoning-large-language-models","title":"Watch Out for Your Guidance on Generation! Exploring Conditional Backdoor Attacks against Large Language Models","date":"2024-04-23","arxiv_id":"2404.14795","n_code_links":0,"syntology":null},{"paper":"/paper/the-power-of-the-noisy-channel-unsupervised","slug":"the-power-of-the-noisy-channel-unsupervised","title":"Unsupervised End-to-End Task-Oriented Dialogue with LLMs: The Power of the Noisy Channel","date":"2024-04-23","arxiv_id":"2404.15219","n_code_links":1,"syntology":null},{"paper":"/paper/automated-long-answer-grading-with-ricechem","slug":"automated-long-answer-grading-with-ricechem","title":"Automated Long Answer Grading with RiceChem Dataset","date":"2024-04-22","arxiv_id":"2404.14316","n_code_links":1,"syntology":null},{"paper":null,"slug":"generating-attractive-and-authentic","title":"Generating Attractive and Authentic Copywriting from Customer Reviews","date":"2024-04-22","arxiv_id":"2404.13906","n_code_links":0,"syntology":null},{"paper":"/paper/how-well-can-llms-echo-us-evaluating-ai","slug":"how-well-can-llms-echo-us-evaluating-ai","title":"How Well Can LLMs Echo Us? Evaluating AI Chatbots' Role-Play Ability with ECHO","date":"2024-04-22","arxiv_id":"2404.13957","n_code_links":1,"syntology":{"ran":6,"of":6,"n_ran_checked":0,"n_instrument":6,"unverified":0,"pointer_only":6,"phrase":"6 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; 6 where Syntology's instrument failed) · 0 unverified","official":{"repos":["cuhk-arise/echo"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"information-re-organization-improves","title":"Information Re-Organization Improves Reasoning in Large Language Models","date":"2024-04-22","arxiv_id":"2404.13985","n_code_links":0,"syntology":null},{"paper":null,"slug":"navigating-the-path-of-writing-outline-guided","title":"Navigating the Path of Writing: Outline-guided Text Generation with Large Language Models","date":"2024-04-22","arxiv_id":"2404.13919","n_code_links":0,"syntology":null},{"paper":"/paper/phi-3-technical-report-a-highly-capable","slug":"phi-3-technical-report-a-highly-capable","title":"Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone","date":"2024-04-22","arxiv_id":"2404.14219","n_code_links":0,"syntology":null},{"paper":"/paper/svgeditbench-a-benchmark-dataset-for","slug":"svgeditbench-a-benchmark-dataset-for","title":"SVGEditBench: A Benchmark Dataset for Quantitative Assessment of LLM's SVG Editing Capabilities","date":"2024-04-21","arxiv_id":"2404.13710","n_code_links":1,"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":["mti-lab/svgeditbench"],"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":"data-alignment-for-zero-shot-concept","title":"Data Alignment for Zero-Shot Concept Generation in Dermatology AI","date":"2024-04-19","arxiv_id":"2404.13043","n_code_links":0,"syntology":null},{"paper":"/paper/dubo-sql-diverse-retrieval-augmented","slug":"dubo-sql-diverse-retrieval-augmented","title":"Dubo-SQL: Diverse Retrieval-Augmented Generation and Fine Tuning for Text-to-SQL","date":"2024-04-19","arxiv_id":"2404.12560","n_code_links":1,"syntology":null}],"record_sha256":"829acf6c699ebe1d12912a8e137b9db629dfa901e9fa4efed0946e4dbfda59fa","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}