{"list":"other","total":54,"rows":[{"id":"1910.10683","title":"Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer","created":"2019-10-23","cited_by_count":14599},{"id":"2306.05685","title":"Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena","created":"2023-06-09","cited_by_count":8626},{"id":"1904.09675","title":"BERTScore: Evaluating Text Generation with BERT","created":"2019-04-21","cited_by_count":5512},{"id":"2307.03172","title":"Lost in the Middle: How Language Models Use Long Contexts","created":"2023-07-06","cited_by_count":3455},{"id":"2303.16634","title":"G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment","created":"2023-03-29","cited_by_count":2276},{"id":"2411.15594","title":"A Survey on LLM-as-a-Judge","created":"2024-11-23","cited_by_count":1423},{"id":"2303.15056","title":"ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks","created":"2023-03-27","cited_by_count":870},{"id":"2404.13076","title":"LLM Evaluators Recognize and Favor Their Own Generations","created":"2024-04-15","cited_by_count":686},{"id":"1901.04085","title":"Passage Re-ranking with BERT","created":"2019-01-13","cited_by_count":645},{"id":"2309.00770","title":"Bias and Fairness in Large Language Models: A Survey","created":"2023-09-02","cited_by_count":639},{"id":"1908.08345","title":"Text Summarization with Pretrained Encoders","created":"2019-08-22","cited_by_count":556},{"id":"1909.02622","title":"MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance","created":"2019-09-05","cited_by_count":418},{"id":"2306.04751","title":"How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources","created":"2023-06-07","cited_by_count":408},{"id":"2003.06713","title":"Document Ranking with a Pretrained Sequence-to-Sequence Model","created":"2020-03-14","cited_by_count":376},{"id":"2304.09542","title":"Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents","created":"2023-04-19","cited_by_count":360},{"id":"2306.17563","title":"Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting","created":"2023-06-30","cited_by_count":307},{"id":"2106.13219","title":"Towards Understanding and Mitigating Social Biases in Language Models","created":"2021-06-24","cited_by_count":296},{"id":"2003.07820","title":"Overview of the TREC 2019 deep learning track","created":"2020-03-17","cited_by_count":286},{"id":"2202.06991","title":"Transformer Memory as a Differentiable Search Index","created":"2022-02-14","cited_by_count":247},{"id":"2209.11755","title":"Promptagator: Few-shot Dense Retrieval From 8 Examples","created":"2022-09-23","cited_by_count":235},{"id":"2102.07662","title":"Overview of the TREC 2020 deep learning track","created":"2021-02-15","cited_by_count":196},{"id":"2204.07496","title":"Improving Passage Retrieval with Zero-Shot Question Generation","created":"2022-04-15","cited_by_count":165},{"id":"2405.01724","title":"Large Language Models are Inconsistent and Biased Evaluators","created":"2024-05-02","cited_by_count":153},{"id":"2403.16950","title":"Aligning with Human Judgement: The Role of Pairwise Preference in Large Language Model Evaluators","created":"2024-03-25","cited_by_count":149},{"id":"2402.07319","title":"ODIN: Disentangled Reward Mitigates Hacking in RLHF","created":"2024-02-11","cited_by_count":136},{"id":"2309.10621","title":"Large language models can accurately predict searcher preferences","created":"2023-09-19","cited_by_count":131},{"id":"2210.10634","title":"RankT5: Fine-Tuning T5 for Text Ranking with Ranking Losses","created":"2022-10-12","cited_by_count":115},{"id":"2410.12954","title":"A Note on Shumailov et al. (2024): `AI Models Collapse When Trained on Recursively Generated Data'","created":"2024-10-16","cited_by_count":115},{"id":"2402.11436","title":"Pride and Prejudice: LLM Amplifies Self-Bias in Self-Refinement","created":"2024-02-18","cited_by_count":114},{"id":"2404.11457","title":"Bias and Unfairness in Information Retrieval Systems: New Challenges in the LLM Era","created":"2024-04-17","cited_by_count":98},{"id":"2311.09766","title":"LLMs as Narcissistic Evaluators: When Ego Inflates Evaluation Scores","created":"2023-11-16","cited_by_count":90},{"id":"2304.09161","title":"Perspectives on Large Language Models for Relevance Judgment","created":"2023-04-13","cited_by_count":85},{"id":"2101.05667","title":"The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models","created":"2021-01-14","cited_by_count":81},{"id":"2407.10817","title":"Foundational Autoraters: Taming Large Language Models for Better Automatic Evaluation","created":"2024-07-15","cited_by_count":74},{"id":"2202.05144","title":"InPars: Data Augmentation for Information Retrieval using Large Language Models","created":"2022-02-10","cited_by_count":62},{"id":"2501.03200","title":"The FACTS Grounding Leaderboard: Benchmarking LLMs' Ability to Ground Responses to Long-Form Input","created":"2025-01-06","cited_by_count":52},{"id":"2502.09747","title":"The Widespread Adoption of Large Language Model-Assisted Writing Across Society","created":"2025-02-13","cited_by_count":51},{"id":"2406.06519","title":"UMBRELA: UMbrela is the (Open-Source Reproduction of the) Bing RELevance Assessor","created":"2024-06-10","cited_by_count":42},{"id":"2310.20501","title":"Neural Retrievers are Biased Towards LLM-Generated Content","created":"2023-10-31","cited_by_count":38},{"id":"2302.11266","title":"One-Shot Labeling for Automatic Relevance Estimation","created":"2023-02-22","cited_by_count":31},{"id":"2412.17156","title":"LLM-based relevance assessment still can't replace human relevance assessment","created":"2024-12-22","cited_by_count":27},{"id":"2409.15133","title":"Don't Use LLMs to Make Relevance Judgments","created":"2024-09-23","cited_by_count":26},{"id":"2405.13003","title":"A Survey on Recent Advances in Conversational Data Generation","created":"2024-05-12","cited_by_count":23},{"id":"2409.16022","title":"AI Can Be Cognitively Biased: An Exploratory Study on Threshold Priming in LLM-Based Batch Relevance Assessment","created":"2024-09-24","cited_by_count":20},{"id":"2411.08275","title":"A Large-Scale Study of Relevance Assessments with Large Language Models: An Initial Look","created":"2024-11-13","cited_by_count":18},{"id":"2405.05600","title":"Can We Use Large Language Models to Fill Relevance Judgment Holes?","created":"2024-05-09","cited_by_count":17},{"id":"2501.17969","title":"LLMs can be Fooled into Labelling a Document as Relevant (best caf\\'e near me; this paper is perfectly relevant)","created":"2025-01-29","cited_by_count":10},{"id":"2407.02464","title":"Reliable Confidence Intervals for Information Retrieval Evaluation Using Generative A.I","created":"2024-07-02","cited_by_count":9},{"id":"2408.16312","title":"SynDL: A Large-Scale Synthetic Test Collection for Passage Retrieval","created":"2024-08-29","cited_by_count":9},{"id":"2406.00247","title":"Large Language Models for Relevance Judgment in Product Search","created":"2024-06-01","cited_by_count":8},{"id":"2408.05388","title":"Report on the 1st Workshop on Large Language Model for Evaluation in Information Retrieval (LLM4Eval 2024) at SIGIR 2024","created":"2024-08-09","cited_by_count":8},{"id":"2501.18536","title":"Illusions of Relevance: Arbitrary Content Injection Attacks Deceive Retrievers, Rerankers, and LLM Judges","created":"2025-01-30","cited_by_count":5},{"id":"2502.15990","title":"Automated Query-Product Relevance Labeling using Large Language Models for E-commerce Search","created":"2025-02-21","cited_by_count":5},{"id":"2403.07654","title":"Analyzing Adversarial Attacks on Sequence-to-Sequence Relevance Models","created":"2024-03-12","cited_by_count":3}],"next_cursor":null}