{"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/weight-decay/papers/29","list_of":"/method/weight-decay","method":"Weight Decay","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":29,"pages_in_order":108,"rows_per_page":100,"rows":[2801,2900],"of":10713,"counts":{"archive_papers_tagged":10713,"with_a_code_link":4533,"where_syntology_ran_a_sample":1291,"not_listed_spam_title":0,"listed":10713,"listed_where_code_ran":1291,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1064,"every_run_a_failure_of_syntologys_instrument":227,"listed_with_a_run_with_no_instrument_failure":1064,"listed_every_run_a_failure_of_syntologys_instrument":227,"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/weight-decay","prev":"/method/weight-decay/papers/28","next":"/method/weight-decay/papers/30","papers":[{"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":null,"slug":"control-token-with-dense-passage-retrieval","title":"Control Token with Dense Passage Retrieval","date":"2024-05-13","arxiv_id":"2405.13008","n_code_links":0,"syntology":null},{"paper":"/paper/evaluation-of-retrieval-augmented-generation","slug":"evaluation-of-retrieval-augmented-generation","title":"Evaluation of Retrieval-Augmented Generation: A Survey","date":"2024-05-13","arxiv_id":"2405.07437","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":null,"slug":"from-questions-to-insightful-answers-building","title":"From Questions to Insightful Answers: Building an Informed Chatbot for University Resources","date":"2024-05-13","arxiv_id":"2405.08120","n_code_links":0,"syntology":null},{"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":"duetrag-collaborative-retrieval-augmented","title":"DuetRAG: Collaborative Retrieval-Augmented Generation","date":"2024-05-12","arxiv_id":"2405.13002","n_code_links":0,"syntology":null},{"paper":null,"slug":"explainabledetector-exploring-transformer","title":"ExplainableDetector: Exploring Transformer-based Language Modeling Approach for SMS Spam Detection with Explainability Analysis","date":"2024-05-12","arxiv_id":"2405.08026","n_code_links":0,"syntology":null},{"paper":null,"slug":"l-u-pin-llm-based-political-ideology","title":"L(u)PIN: LLM-based Political Ideology Nowcasting","date":"2024-05-12","arxiv_id":"2405.07320","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":"tacoere-cluster-aware-compression-for-event","title":"TacoERE: Cluster-aware Compression for Event Relation Extraction","date":"2024-05-11","arxiv_id":"2405.06890","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-partial-survey-of-decentralized-cooperative","title":"An Initial Introduction to Cooperative Multi-Agent Reinforcement Learning","date":"2024-05-10","arxiv_id":"2405.06161","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-survey-on-rag-meets-llms-towards-retrieval","title":"A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models","date":"2024-05-10","arxiv_id":"2405.06211","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":"canal-cyber-activity-news-alerting-language","title":"CANAL -- Cyber Activity News Alerting Language Model: Empirical Approach vs. Expensive LLM","date":"2024-05-10","arxiv_id":"2405.06772","n_code_links":0,"syntology":null},{"paper":null,"slug":"characterizing-the-accuracy-efficiency-trade","title":"Characterizing the Accuracy -- Efficiency Trade-off of Low-rank Decomposition in Language Models","date":"2024-05-10","arxiv_id":"2405.06626","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":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":"/paper/ditto-quantization-aware-secure-inference-of","slug":"ditto-quantization-aware-secure-inference-of","title":"Ditto: Quantization-aware Secure Inference of Transformers upon MPC","date":"2024-05-09","arxiv_id":"2405.05525","n_code_links":1,"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":"evaluating-students-open-ended-written","title":"Evaluating Students' Open-ended Written Responses with LLMs: Using the RAG Framework for GPT-3.5, GPT-4, Claude-3, and Mistral-Large","date":"2024-05-08","arxiv_id":"2405.05444","n_code_links":0,"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":"utilizing-large-language-models-to-generate","title":"Utilizing Large Language Models to Generate Synthetic Data to Increase the Performance of BERT-Based Neural Networks","date":"2024-05-08","arxiv_id":"2405.06695","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":"/paper/enriched-bert-embeddings-for-scholarly","slug":"enriched-bert-embeddings-for-scholarly","title":"Enriched BERT Embeddings for Scholarly Publication Classification","date":"2024-05-07","arxiv_id":"2405.04136","n_code_links":1,"syntology":null},{"paper":null,"slug":"eratta-extreme-rag-for-table-to-answers-with","title":"ERATTA: Extreme RAG for Table To Answers with Large Language Models","date":"2024-05-07","arxiv_id":"2405.03963","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":"remote-diffusion","title":"Remote Diffusion","date":"2024-05-07","arxiv_id":"2405.04717","n_code_links":0,"syntology":null},{"paper":"/paper/revisiting-character-level-adversarial","slug":"revisiting-character-level-adversarial","title":"Revisiting Character-level Adversarial Attacks for Language Models","date":"2024-05-07","arxiv_id":"2405.04346","n_code_links":1,"syntology":{"ran":23,"of":31,"n_ran_checked":13,"n_instrument":10,"unverified":8,"pointer_only":0,"phrase":"23 ran (of which 3 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 10 where Syntology's instrument failed) · 8 unverified","official":{"repos":["lions-epfl/charmer"],"state":"official (archive's flag): 23 ran","n_ran":23,"n_constructed":3,"n_ran_no_instrument_failure":13,"n_unverified":8,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"robust-implementation-of-retrieval-augmented","title":"Robust Implementation of Retrieval-Augmented Generation on Edge-based Computing-in-Memory Architectures","date":"2024-05-07","arxiv_id":"2405.04700","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":null,"slug":"utilizing-gpt-to-enhance-text-summarization-a","title":"Utilizing GPT to Enhance Text Summarization: A Strategy to Minimize Hallucinations","date":"2024-05-07","arxiv_id":"2405.04039","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":null,"slug":"characterizing-the-dilemma-of-performance-and","title":"Characterizing the Dilemma of Performance and Index Size in Billion-Scale Vector Search and Breaking It with Second-Tier Memory","date":"2024-05-06","arxiv_id":"2405.03267","n_code_links":0,"syntology":null},{"paper":null,"slug":"compressing-long-context-for-enhancing-rag","title":"Compressing Long Context for Enhancing RAG with AMR-based Concept Distillation","date":"2024-05-06","arxiv_id":"2405.03085","n_code_links":0,"syntology":null},{"paper":null,"slug":"detecting-android-malware-from-neural","title":"Detecting Android Malware: From Neural Embeddings to Hands-On Validation with BERTroid","date":"2024-05-06","arxiv_id":"2405.03620","n_code_links":0,"syntology":null},{"paper":null,"slug":"detecting-anti-semitic-hate-speech-using","title":"Detecting Anti-Semitic Hate Speech using Transformer-based Large Language Models","date":"2024-05-06","arxiv_id":"2405.03794","n_code_links":0,"syntology":null},{"paper":"/paper/eragent-enhancing-retrieval-augmented","slug":"eragent-enhancing-retrieval-augmented","title":"ERAGent: Enhancing Retrieval-Augmented Language Models with Improved Accuracy, Efficiency, and Personalization","date":"2024-05-06","arxiv_id":"2405.06683","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":"/paper/structure-preserving-network-compression-via","slug":"structure-preserving-network-compression-via","title":"Structure-Preserving Network Compression Via Low-Rank Induced Training Through Linear Layers Composition","date":"2024-05-06","arxiv_id":"2405.03089","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":"leveraging-lecture-content-for-improved","title":"Leveraging Lecture Content for Improved Feedback: Explorations with GPT-4 and Retrieval Augmented Generation","date":"2024-05-05","arxiv_id":"2405.06681","n_code_links":0,"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":null,"slug":"stochastic-rag-end-to-end-retrieval-augmented","title":"Stochastic RAG: End-to-End Retrieval-Augmented Generation through Expected Utility Maximization","date":"2024-05-05","arxiv_id":"2405.02816","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":"a-combination-of-bert-and-transformer-for","title":"A Combination of BERT and Transformer for Vietnamese Spelling Correction","date":"2024-05-04","arxiv_id":"2405.02573","n_code_links":0,"syntology":null},{"paper":null,"slug":"analyzing-narrative-processing-in-large","title":"Analyzing Narrative Processing in Large Language Models (LLMs): Using GPT4 to test BERT","date":"2024-05-03","arxiv_id":"2405.02024","n_code_links":0,"syntology":null},{"paper":null,"slug":"comparative-analysis-of-retrieval-systems-in","title":"Comparative Analysis of Retrieval Systems in the Real World","date":"2024-05-03","arxiv_id":"2405.02048","n_code_links":0,"syntology":null},{"paper":"/paper/dallmi-domain-adaption-for-llm-based-multi","slug":"dallmi-domain-adaption-for-llm-based-multi","title":"DALLMi: Domain Adaption for LLM-based Multi-label Classifier","date":"2024-05-03","arxiv_id":"2405.01883","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":"/paper/exploiting-chatgpt-for-diagnosing-autism","slug":"exploiting-chatgpt-for-diagnosing-autism","title":"Exploiting ChatGPT for Diagnosing Autism-Associated Language Disorders and Identifying Distinct Features","date":"2024-05-03","arxiv_id":"2405.01799","n_code_links":1,"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/structural-pruning-of-pre-trained-language","slug":"structural-pruning-of-pre-trained-language","title":"Structural Pruning of Pre-trained Language Models via Neural Architecture Search","date":"2024-05-03","arxiv_id":"2405.02267","n_code_links":1,"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":"progressive-feedforward-collapse-of-resnet","title":"Progressive Feedforward Collapse of ResNet Training","date":"2024-05-02","arxiv_id":"2405.00985","n_code_links":0,"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":"a-named-entity-recognition-and-topic-modeling","title":"A Named Entity Recognition and Topic Modeling-based Solution for Locating and Better Assessment of Natural Disasters in Social Media","date":"2024-05-01","arxiv_id":"2405.00903","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/integrating-a-i-in-higher-education-protocol","slug":"integrating-a-i-in-higher-education-protocol","title":"Integrating A.I. in Higher Education: Protocol for a Pilot Study with 'SAMCares: An Adaptive Learning Hub'","date":"2024-05-01","arxiv_id":"2405.00330","n_code_links":1,"syntology":null},{"paper":"/paper/opinion-mining-using-pre-trained-large","slug":"opinion-mining-using-pre-trained-large","title":"Opinion Mining Using Pre-Trained Large Language Models: Identifying the Type, Polarity, Intensity, Expression, and Source of Private States","date":"2024-05-01","arxiv_id":null,"n_code_links":1,"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/graph-neural-network-approach-to-semantic","slug":"graph-neural-network-approach-to-semantic","title":"Graph Neural Network Approach to Semantic Type Detection in Tables","date":"2024-04-30","arxiv_id":"2405.00123","n_code_links":1,"syntology":null},{"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":"/paper/towards-a-search-engine-for-machines-unified","slug":"towards-a-search-engine-for-machines-unified","title":"Towards a Search Engine for Machines: Unified Ranking for Multiple Retrieval-Augmented Large Language Models","date":"2024-04-30","arxiv_id":"2405.00175","n_code_links":1,"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":"federa-efficient-fine-tuning-of-language","title":"FeDeRA:Efficient Fine-tuning of Language Models in Federated Learning Leveraging Weight Decomposition","date":"2024-04-29","arxiv_id":"2404.18848","n_code_links":0,"syntology":null}],"record_sha256":"6aa52ff12c0ecabeba53025175878d77ff2f346eda0f066b9bae307ed1dd4fb8","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}