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Query generation is a critical task for web search engines (e.g.
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Constitutional ai: Harmlessness from ai feedback
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Solving math word problems with process-and outcome-based feedback
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AUC Maximization in the Era of Big Data and AI: A Survey
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Tallrec: An effective and efficient tuning framework to align large language model with recommendation. In Proceedings of the 17th ACM Conference on Recommender Systems . 1007–1014
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Judging llm-as-a-judge with mt-bench and chatbot arena
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Knowledge-Augmented Large Language Models for Personalized Contextual Query Suggestion
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Drlc: Reinforcement learning with dense rewards from llm critic
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Humans or llms as the judge? a study on judgement biases
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Openagi: When llm meets domain experts
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Robert Kirk, Ishita Mediratta, Christoforos Nalmpantis, Jelena Luketina, Eric Hambro, Edward Grefenstette, and Roberta Raileanu. 2023 · 2023
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Rlaif: Scaling reinforcement learning from human feedback with ai feedback
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GPT4Rec: A generative framework for personalized recommendation and user interests interpretation
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Llama 2: Open foundation and fine-tuned chat models
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Survey on factuality in large language models: Knowledge, retrieval and domain-specificity
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Fine-Grained Human Feedback Gives Better Rewards for Language Model Training
Zeqiu Wu, Yushi Hu, Weijia Shi, Nouha Dziri, Alane Suhr, Prithviraj Ammanabrolu, Noah A. Smith, Mari Ostendorf, and Hannaneh Hajishirzi. 2023 · 2023
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Empower Large Language Model to Perform Better on Industrial Domain-Specific Question Answering. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: EMNLP 2023 - Industry Track, Singapore, December 6-10, 2023 , Mingxuan Wang and Imed Zitouni (Eds.). Association for Computational Linguistics, 294–312
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Beyond Imitation: Leveraging Fine-grained Quality Signals for Alignment
Geyang Guo, Ranchi Zhao, Tianyi Tang, Wayne Xin Zhao, and Ji-Rong Wen. 2024 · 2024
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A Survey of Generative Search and Recommendation in the Era of Large Language Models
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How Can Recommender Systems Benefit from Large Language Models: A Survey
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Direct preference optimization: Your language model is secretly a reward model
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Can Small Language Models be Good Reasoners for Sequential Recommendation?. In Proceedings of the ACM on Web Conference 2024 . 3876–3887
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Llmrec: Large language models with graph augmentation for recommendation. In Proceedings of the 17th ACM International Conference on Web Search and Data Mining . 806–815
Wei Wei, Xubin Ren, Jiabin Tang, Qinyong Wang, Lixin Su, Suqi Cheng, Junfeng Wang, Dawei Yin, and Chao Huang. 2024 · 2024
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A survey on large language models for recommendation
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Aligning Large Language Models via Fine-grained Supervision
Dehong Xu, Liang Qiu, Minseok Kim, Faisal Ladhak, and Jaeyoung Do. 2024 · 2024
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Token-level Direct Preference Optimization
Yongcheng Zeng, Guoqing Liu, Weiyu Ma, Ning Yang, Haifeng Zhang, and Jun Wang. 2024 · 2024
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Dpo meets ppo: Reinforced token optimization for rlhf
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