{"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/large-language-model/papers/61","list_of":"/task/large-language-model","task":"Large Language Model","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":61,"pages_in_order":61,"rows_per_page":100,"rows":[6001,6097],"of":6097,"counts":{"archive_papers_tagged":6097,"with_a_code_link":2250,"where_syntology_ran_a_sample":801,"not_listed_spam_title":0,"listed":6097,"listed_where_code_ran":801,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":683,"every_run_a_failure_of_syntologys_instrument":118,"listed_with_a_run_with_no_instrument_failure":683,"listed_every_run_a_failure_of_syntologys_instrument":118,"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/large-language-model","prev":"/task/large-language-model/papers/60","next":null,"papers":[{"url":null,"slug":"simfluence-modeling-the-influence-of","title":"Simfluence: Modeling the Influence of Individual Training Examples by Simulating Training Runs","date":"2023-03-14","arxiv_id":"2303.08114","repositories_listed":0,"syntology":null},{"url":null,"slug":"odin-on-demand-data-formulation-to-mitigate","title":"ODIN: On-demand Data Formulation to Mitigate Dataset Lock-in","date":"2023-03-13","arxiv_id":"2303.06832","repositories_listed":0,"syntology":null},{"url":null,"slug":"consistency-analysis-of-chatgpt","title":"Consistency Analysis of ChatGPT","date":"2023-03-11","arxiv_id":"2303.06273","repositories_listed":0,"syntology":null},{"url":null,"slug":"susceptibility-to-influence-of-large-language","title":"Susceptibility to Influence of Large Language Models","date":"2023-03-10","arxiv_id":"2303.06074","repositories_listed":0,"syntology":null},{"url":null,"slug":"planning-with-large-language-models-for-code","title":"Planning with Large Language Models for Code Generation","date":"2023-03-09","arxiv_id":"2303.05510","repositories_listed":0,"syntology":null},{"url":null,"slug":"weakly-supervised-hoi-detection-from","title":"Weakly-Supervised HOI Detection from Interaction Labels Only and Language/Vision-Language Priors","date":"2023-03-09","arxiv_id":"2303.05546","repositories_listed":0,"syntology":null},{"url":null,"slug":"chatgpt-is-on-the-horizon-could-a-large","title":"ChatGPT is on the Horizon: Could a Large Language Model be Suitable for Intelligent Traffic Safety Research and Applications?","date":"2023-03-06","arxiv_id":"2303.05382","repositories_listed":0,"syntology":null},{"url":null,"slug":"foundationtts-text-to-speech-for-asr","title":"FoundationTTS: Text-to-Speech for ASR Customization with Generative Language Model","date":"2023-03-06","arxiv_id":"2303.02939","repositories_listed":0,"syntology":null},{"url":null,"slug":"could-a-large-language-model-be-conscious","title":"Could a Large Language Model be Conscious?","date":"2023-03-04","arxiv_id":"2303.07103","repositories_listed":0,"syntology":null},{"url":null,"slug":"ai-and-the-fci-can-chatgpt-project-an","title":"AI and the FCI: Can ChatGPT Project an Understanding of Introductory Physics?","date":"2023-03-02","arxiv_id":"2303.01067","repositories_listed":0,"syntology":null},{"url":null,"slug":"almanac-knowledge-grounded-language-models","title":"Almanac: Retrieval-Augmented Language Models for Clinical Medicine","date":"2023-03-01","arxiv_id":"2303.01229","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-large-language-model-and-story","title":"Leveraging Large Language Model and Story-Based Gamification in Intelligent Tutoring System to Scaffold Introductory Programming Courses: A Design-Based Research Study","date":"2023-02-25","arxiv_id":"2302.12834","repositories_listed":0,"syntology":null},{"url":null,"slug":"factual-consistency-oriented-speech","title":"Factual Consistency Oriented Speech Recognition","date":"2023-02-24","arxiv_id":"2302.12369","repositories_listed":0,"syntology":null},{"url":null,"slug":"privately-customizing-prefinetuning-to-better","title":"Privately Customizing Prefinetuning to Better Match User Data in Federated Learning","date":"2023-02-17","arxiv_id":"2302.09042","repositories_listed":0,"syntology":null},{"url":null,"slug":"prompting-large-language-models-with-the","title":"Prompting Large Language Models With the Socratic Method","date":"2023-02-17","arxiv_id":"2303.08769","repositories_listed":0,"syntology":null},{"url":null,"slug":"ai-chat-assistants-can-improve-conversations","title":"AI Chat Assistants can Improve Conversations about Divisive Topics","date":"2023-02-14","arxiv_id":"2302.07268","repositories_listed":0,"syntology":null},{"url":null,"slug":"artificial-intelligence-in-psychology","title":"Diminished Diversity-of-Thought in a Standard Large Language Model","date":"2023-02-13","arxiv_id":"2302.07267","repositories_listed":0,"syntology":null},{"url":null,"slug":"zero-shot-generation-of-coherent-storybook","title":"Zero-shot Generation of Coherent Storybook from Plain Text Story using Diffusion Models","date":"2023-02-08","arxiv_id":"2302.03900","repositories_listed":0,"syntology":null},{"url":null,"slug":"witscript-2-a-system-for-generating","title":"Witscript 2: A System for Generating Improvised Jokes Without Wordplay","date":"2023-02-03","arxiv_id":"2302.03036","repositories_listed":0,"syntology":null},{"url":null,"slug":"creating-a-large-language-model-of-a","title":"Creating a Large Language Model of a Philosopher","date":"2023-02-02","arxiv_id":"2302.01339","repositories_listed":0,"syntology":null},{"url":null,"slug":"pseudo-3d-perception-transformer-with-multi","title":"Multi-modal Large Language Model Enhanced Pseudo 3D Perception Framework for Visual Commonsense Reasoning","date":"2023-01-30","arxiv_id":"2301.13335","repositories_listed":0,"syntology":null},{"url":null,"slug":"truth-machines-synthesizing-veracity-in-ai","title":"Truth Machines: Synthesizing Veracity in AI Language Models","date":"2023-01-28","arxiv_id":"2301.12066","repositories_listed":0,"syntology":null},{"url":null,"slug":"theme-driven-keyphrase-extraction-from-social","title":"Theme-driven Keyphrase Extraction to Analyze Social Media Discourse","date":"2023-01-27","arxiv_id":"2301.11508","repositories_listed":0,"syntology":null},{"url":null,"slug":"task-formulation-for-extracting-social","title":"Task formulation for Extracting Social Determinants of Health from Clinical Narratives","date":"2023-01-26","arxiv_id":"2301.11386","repositories_listed":0,"syntology":null},{"url":null,"slug":"explaining-large-language-model-based-neural","title":"Explaining Large Language Model-Based Neural Semantic Parsers (Student Abstract)","date":"2023-01-25","arxiv_id":"2301.13820","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-language-models-can-segment-narrative","title":"Large language models can segment narrative events similarly to humans","date":"2023-01-24","arxiv_id":"2301.10297","repositories_listed":0,"syntology":null},{"url":null,"slug":"prompting-large-language-model-for-machine","title":"Prompting Large Language Model for Machine Translation: A Case Study","date":"2023-01-17","arxiv_id":"2301.07069","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-case-study-in-engineering-a-conversational","title":"A Case Study in Engineering a Conversational Programming Assistant's Persona","date":"2023-01-13","arxiv_id":"2301.10016","repositories_listed":0,"syntology":null},{"url":null,"slug":"chatbots-in-a-honeypot-world","title":"Chatbots in a Honeypot World","date":"2023-01-10","arxiv_id":"2301.03771","repositories_listed":0,"syntology":null},{"url":null,"slug":"memory-augmented-large-language-models-are","title":"Memory Augmented Large Language Models are Computationally Universal","date":"2023-01-10","arxiv_id":"2301.04589","repositories_listed":0,"syntology":null},{"url":null,"slug":"measuring-an-artificial-intelligence-agent-s","title":"Measuring an artificial intelligence agent's trust in humans using machine incentives","date":"2022-12-27","arxiv_id":"2212.13371","repositories_listed":0,"syntology":null},{"url":null,"slug":"what-do-llms-know-about-financial-markets-a","title":"What do LLMs Know about Financial Markets? A Case Study on Reddit Market Sentiment Analysis","date":"2022-12-21","arxiv_id":"2212.11311","repositories_listed":0,"syntology":null},{"url":null,"slug":"improved-long-form-spoken-language","title":"Improved Long-Form Spoken Language Translation with Large Language Models","date":"2022-12-19","arxiv_id":"2212.09895","repositories_listed":0,"syntology":null},{"url":null,"slug":"structured-information-extraction-from","title":"Structured information extraction from complex scientific text with fine-tuned large language models","date":"2022-12-10","arxiv_id":"2212.05238","repositories_listed":0,"syntology":null},{"url":null,"slug":"pre-training-with-scientific-text-improves","title":"Pre-Training With Scientific Text Improves Educational Question Generation","date":"2022-12-07","arxiv_id":"2212.03869","repositories_listed":0,"syntology":null},{"url":null,"slug":"i2mvformer-large-language-model-generated","title":"I2MVFormer: Large Language Model Generated Multi-View Document Supervision for Zero-Shot Image Classification","date":"2022-12-05","arxiv_id":"2212.02291","repositories_listed":0,"syntology":null},{"url":null,"slug":"legal-prompt-engineering-for-multilingual","title":"Legal Prompt Engineering for Multilingual Legal Judgement Prediction","date":"2022-12-05","arxiv_id":"2212.02199","repositories_listed":0,"syntology":null},{"url":null,"slug":"extensible-prompts-for-language-models","title":"Extensible Prompts for Language Models on Zero-shot Language Style Customization","date":"2022-12-01","arxiv_id":"2212.00616","repositories_listed":0,"syntology":null},{"url":null,"slug":"gpt-3-driven-pedagogical-agents-for-training","title":"GPT-3-driven pedagogical agents for training children's curious question-asking skills","date":"2022-11-25","arxiv_id":"2211.14228","repositories_listed":0,"syntology":null},{"url":null,"slug":"deanthropomorphising-nlp-can-a-language-model","title":"Deanthropomorphising NLP: Can a Language Model Be Conscious?","date":"2022-11-21","arxiv_id":"2211.11483","repositories_listed":0,"syntology":null},{"url":null,"slug":"planning-with-large-language-models-via","title":"CAPE: Corrective Actions from Precondition Errors using Large Language Models","date":"2022-11-17","arxiv_id":"2211.09935","repositories_listed":0,"syntology":null},{"url":null,"slug":"introducing-semantics-into-speech-encoders","title":"Introducing Semantics into Speech Encoders","date":"2022-11-15","arxiv_id":"2211.08402","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-a-mathematics-formalisation-assistant","title":"Towards a Mathematics Formalisation Assistant using Large Language Models","date":"2022-11-14","arxiv_id":"2211.07524","repositories_listed":0,"syntology":null},{"url":null,"slug":"prompt-learning-for-domain-adaptation-in-task","title":"Prompt Learning for Domain Adaptation in Task-Oriented Dialogue","date":"2022-11-10","arxiv_id":"2211.05596","repositories_listed":0,"syntology":null},{"url":null,"slug":"prompter-utilizing-large-language-model","title":"Prompter: Utilizing Large Language Model Prompting for a Data Efficient Embodied Instruction Following","date":"2022-11-07","arxiv_id":"2211.03267","repositories_listed":0,"syntology":null},{"url":null,"slug":"measuring-progress-on-scalable-oversight-for","title":"Measuring Progress on Scalable Oversight for Large Language Models","date":"2022-11-04","arxiv_id":"2211.03540","repositories_listed":0,"syntology":null},{"url":null,"slug":"preserving-in-context-learning-ability-in","title":"Two-stage LLM Fine-tuning with Less Specialization and More Generalization","date":"2022-11-01","arxiv_id":"2211.00635","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-simple-yet-effective-approach-to-finding","title":"A Simple, Yet Effective Approach to Finding Biases in Code Generation","date":"2022-10-31","arxiv_id":"2211.00609","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-better-intent-representations-for","title":"Learning Better Intent Representations for Financial Open Intent Classification","date":"2022-10-25","arxiv_id":"2210.14304","repositories_listed":0,"syntology":null},{"url":"/paper/transcending-scaling-laws-with-0-1-extra","slug":"transcending-scaling-laws-with-0-1-extra","title":"Transcending Scaling Laws with 0.1% Extra Compute","date":"2022-10-20","arxiv_id":"2210.11399","repositories_listed":0,"syntology":null},{"url":null,"slug":"systematicity-in-gpt-3-s-interpretation-of","title":"Systematicity in GPT-3's Interpretation of Novel English Noun Compounds","date":"2022-10-18","arxiv_id":"2210.09492","repositories_listed":0,"syntology":null},{"url":null,"slug":"dungeons-and-dragons-as-a-dialog-challenge","title":"Dungeons and Dragons as a Dialog Challenge for Artificial Intelligence","date":"2022-10-13","arxiv_id":"2210.07109","repositories_listed":0,"syntology":null},{"url":null,"slug":"retrieval-augmentation-for-t5-re-ranker-using","title":"Retrieval Augmentation for T5 Re-ranker using External Sources","date":"2022-10-11","arxiv_id":"2210.05145","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-only-chance-to-understand-machine","title":"The Only Chance to Understand: Machine Translation of the Severely Endangered Low-resource Languages of Eurasia","date":"2022-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"i-speak-you-verify-toward-trustworthy-neural","title":"Toward Trustworthy Neural Program Synthesis","date":"2022-09-29","arxiv_id":"2210.00848","repositories_listed":0,"syntology":null},{"url":null,"slug":"repairing-bugs-in-python-assignments-using","title":"Repairing Bugs in Python Assignments Using Large Language Models","date":"2022-09-29","arxiv_id":"2209.14876","repositories_listed":0,"syntology":null},{"url":null,"slug":"unpacking-large-language-models-with","title":"Unpacking Large Language Models with Conceptual Consistency","date":"2022-09-29","arxiv_id":"2209.15093","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-semantic-understanding-with-self","title":"Enhancing Semantic Understanding with Self-supervised Methods for Abstractive Dialogue Summarization","date":"2022-09-01","arxiv_id":"2209.00278","repositories_listed":0,"syntology":null},{"url":null,"slug":"prefix-embeddings-for-in-context-machine","title":"Prefix Embeddings for In-context Machine Translation","date":"2022-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"training-a-t5-using-lab-sized-resources","title":"Training a T5 Using Lab-sized Resources","date":"2022-08-25","arxiv_id":"2208.12097","repositories_listed":0,"syntology":null},{"url":null,"slug":"repair-is-nearly-generation-multilingual","title":"Repair Is Nearly Generation: Multilingual Program Repair with LLMs","date":"2022-08-24","arxiv_id":"2208.11640","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-diverse-knowledge-sources-for","title":"Integrating Diverse Knowledge Sources for Online One-shot Learning of Novel Tasks","date":"2022-08-19","arxiv_id":"2208.09554","repositories_listed":0,"syntology":null},{"url":null,"slug":"open-information-extraction-from-2007-to-2022","title":"A Survey on Open Information Extraction from Rule-based Model to Large Language Model","date":"2022-08-18","arxiv_id":"2208.08690","repositories_listed":0,"syntology":null},{"url":null,"slug":"reducing-retraining-by-recycling-parameter","title":"Reducing Retraining by Recycling Parameter-Efficient Prompts","date":"2022-08-10","arxiv_id":"2208.05577","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hazard-analysis-framework-for-code","title":"A Hazard Analysis Framework for Code Synthesis Large Language Models","date":"2022-07-25","arxiv_id":"2207.14157","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-overview-of-distant-supervision-for","title":"An Overview of Distant Supervision for Relation Extraction with a Focus on Denoising and Pre-training Methods","date":"2022-07-17","arxiv_id":"2207.08286","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-no-code-low-code-paradigm-for-authoring","title":"A No-Code Low-Code Paradigm for Authoring Business Automations Using Natural Language","date":"2022-07-15","arxiv_id":"2207.10648","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-data-to-text-generation-based-on-small","title":"Neural Data-to-Text Generation Based on Small Datasets: Comparing the Added Value of Two Semi-Supervised Learning Approaches on Top of a Large Language Model","date":"2022-07-14","arxiv_id":"2207.06839","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-cognitive-psychology-to-understand-gpt","title":"Using cognitive psychology to understand GPT-3","date":"2022-06-21","arxiv_id":"2206.14576","repositories_listed":0,"syntology":null},{"url":null,"slug":"know-your-audience-specializing-grounded","title":"Know your audience: specializing grounded language models with listener subtraction","date":"2022-06-16","arxiv_id":"2206.08349","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-generation-of-programming-exercises-1","title":"Automatic Generation of Programming Exercises and Code Explanations using Large Language Models","date":"2022-06-03","arxiv_id":"2206.11861","repositories_listed":0,"syntology":null},{"url":null,"slug":"visual-clues-bridging-vision-and-language","title":"Visual Clues: Bridging Vision and Language Foundations for Image Paragraph Captioning","date":"2022-06-03","arxiv_id":"2206.01843","repositories_listed":0,"syntology":null},{"url":null,"slug":"happenstance-utilizing-semantic-search-to","title":"Happenstance: Utilizing Semantic Search to Track Russian State Media Narratives about the Russo-Ukrainian War On Reddit","date":"2022-05-28","arxiv_id":"2205.14484","repositories_listed":0,"syntology":null},{"url":null,"slug":"differentially-private-decoding-in-large","title":"Differentially Private Decoding in Large Language Models","date":"2022-05-26","arxiv_id":"2205.13621","repositories_listed":0,"syntology":null},{"url":null,"slug":"combining-extraction-and-generation-for","title":"Combining Extraction and Generation for Constructing Belief-Consequence Causal Links","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"extraction-of-sleep-information-from-clinical","title":"Extraction of Sleep Information from Clinical Notes of Patients with Alzheimer's Disease Using Natural Language Processing","date":"2022-03-08","arxiv_id":"2204.09601","repositories_listed":0,"syntology":null},{"url":null,"slug":"pop-quiz-can-a-large-language-model-help-with","title":"Pop Quiz! Can a Large Language Model Help With Reverse Engineering?","date":"2022-02-02","arxiv_id":"2202.01142","repositories_listed":0,"syntology":null},{"url":null,"slug":"codebpe-investigating-subtokenization-options","title":"CodeBPE: Investigating Subtokenization Options for Large Language Model Pretraining on Source Code","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hardness-masking-via-auto-regressive-language","title":"Hardness Masking via Auto-Regressive Language Model","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"computational-lens-on-cognition-study-of","title":"Imagined versus Remembered Stories: Quantifying Differences in Narrative Flow","date":"2022-01-07","arxiv_id":"2201.02662","repositories_listed":0,"syntology":null},{"url":null,"slug":"macberth-development-and-evaluation-of-a","title":"MacBERTh: Development and Evaluation of a Historically Pre-trained Language Model for English (1450-1950)","date":"2021-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-testing-and-debugging-of-nlp-models","title":"Adaptive Testing and Debugging of NLP Models","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"synthbio-a-case-study-in-human-ai","title":"SynthBio: A Case Study in Human-AI Collaborative Curation of Text Datasets","date":"2021-11-11","arxiv_id":"2111.06467","repositories_listed":0,"syntology":null},{"url":null,"slug":"bert2bert-towards-reusable-pretrained","title":"bert2BERT: Towards Reusable Pretrained Language Models","date":"2021-10-14","arxiv_id":"2110.07143","repositories_listed":0,"syntology":null},{"url":null,"slug":"ai-chains-transparent-and-controllable-human","title":"AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts","date":"2021-10-04","arxiv_id":"2110.01691","repositories_listed":0,"syntology":null},{"url":null,"slug":"generate-annotate-and-learn-generative-models-1","title":"Generate, Annotate, and Learn: Generative Models Advance Self-Training and Knowledge Distillation","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"transfer-training-from-smaller-language-model","title":"Transfer training from smaller language model","date":"2021-04-23","arxiv_id":"2104.11390","repositories_listed":0,"syntology":null},{"url":null,"slug":"arabic-compact-language-modelling-for","title":"Arabic Compact Language Modelling for Resource Limited Devices","date":"2021-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"globalizing-bert-based-transformer","title":"Globalizing BERT-based Transformer Architectures for Long Document Summarization","date":"2021-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"story-centaur-large-language-model-few-shot","title":"Story Centaur: Large Language Model Few Shot Learning as a Creative Writing Tool","date":"2021-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-graph-total-variation-regularized-softmax","title":"Graphmax for Text Generation","date":"2021-01-01","arxiv_id":"2101.00153","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-review-of-on-device-fully-neural-end-to-end","title":"A review of on-device fully neural end-to-end automatic speech recognition algorithms","date":"2020-12-14","arxiv_id":"2012.07974","repositories_listed":0,"syntology":null},{"url":null,"slug":"plug-and-play-conversational-models-1","title":"Plug-and-Play Conversational Models","date":"2020-07-23","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"challenge-closed-book-science-exam-a-meta","title":"Challenge Closed-book Science Exam: A Meta-learning Based Question Answering System","date":"2020-04-26","arxiv_id":"2004.12303","repositories_listed":0,"syntology":null},{"url":null,"slug":"compressing-language-models-using-doped","title":"Compressing Language Models using Doped Kronecker Products","date":"2020-01-24","arxiv_id":"2001.08896","repositories_listed":0,"syntology":null},{"url":null,"slug":"paraphrasing-with-large-language-models-1","title":"Paraphrasing with Large Language Models","date":"2019-11-21","arxiv_id":"1911.09661","repositories_listed":0,"syntology":null},{"url":"/paper/enhancing-clinical-concept-extraction-with","slug":"enhancing-clinical-concept-extraction-with","title":"Enhancing Clinical Concept Extraction with Contextual Embeddings","date":"2019-02-22","arxiv_id":"1902.08691","repositories_listed":0,"syntology":null}],"record_sha256":"9c92d173e02fb63c3effb294786ccc709710aae45cc09e3812ffb9ef663c7c5d","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}