When Do LLMs Need Retrieval Augmentation? Mitigating LLMs' Overconfidence Helps Retrieval Augmentation

18 February 2024

Papers citing "When Do LLMs Need Retrieval Augmentation? Mitigating LLMs' Overconfidence Helps Retrieval Augmentation"

8 / 8 papers shown

Title
AI Awareness X. Li Haoyuan Shi Rongwu Xu Wei Xu 54 0 0 25 Apr 2025
Evaluating Implicit Bias in Large Language Models by Attacking From a Psychometric Perspective Yuchen Wen Keping Bi Wei Chen J. Guo Xueqi Cheng 66 1 0 20 Feb 2025
MINTQA: A Multi-Hop Question Answering Benchmark for Evaluating LLMs on New and Tail Knowledge Jie He Nan Hu Wanqiu Long Jiaoyan Chen Jeff Z. Pan ELM LRM 92 6 0 22 Dec 2024
Generate rather than Retrieve: Large Language Models are Strong Context Generators W. Yu Dan Iter Shuohang Wang Yichong Xu Mingxuan Ju Soumya Sanyal Chenguang Zhu Michael Zeng Meng-Long Jiang RALM AIMat 212 318 0 21 Sep 2022
Large Language Models are Zero-Shot Reasoners Takeshi Kojima S. Gu Machel Reid Yutaka Matsuo Yusuke Iwasawa ReLM LRM 291 2,712 0 24 May 2022
Training language models to follow instructions with human feedback Long Ouyang Jeff Wu Xu Jiang Diogo Almeida Carroll L. Wainwright ... Amanda Askell Peter Welinder Paul Christiano Jan Leike Ryan J. Lowe OSLM ALM 301 11,730 0 04 Mar 2022
RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking Ruiyang Ren Yingqi Qu Jing Liu Wayne Xin Zhao Qiaoqiao She Hua-Hong Wu Haifeng Wang Ji-Rong Wen 121 244 0 14 Oct 2021
RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering Yingqi Qu Yuchen Ding Jing Liu Kai Liu Ruiyang Ren Xin Zhao Daxiang Dong Hua-Hong Wu Haifeng Wang RALM OffRL 203 516 0 16 Oct 2020