From Parameters to Prompts: Understanding and Mitigating the Factuality Gap between Fine-Tuned LLMs

29 May 2025

Papers citing "From Parameters to Prompts: Understanding and Mitigating the Factuality Gap between Fine-Tuned LLMs"

8 / 8 papers shown

Title
Supervised Knowledge Makes Large Language Models Better In-context Learners Linyi Yang Shuibai Zhang Zhuohao Yu Guangsheng Bao Yidong Wang ... Ruochen Xu Weirong Ye Xing Xie Weizhu Chen Yue Zhang 62 16 0 26 Dec 2023
Knowledgeable In-Context Tuning: Exploring and Exploiting Factual Knowledge for In-Context Learning Jiadong Wang Chengyu Wang Chuanqi Tan Jun Huang Ming Gao KELM 63 6 0 26 Sep 2023
Can We Edit Factual Knowledge by In-Context Learning? Ce Zheng Lei Li Qingxiu Dong Yuxuan Fan Zhiyong Wu Jingjing Xu Baobao Chang KELM 48 199 0 22 May 2023
Large Language Models Struggle to Learn Long-Tail Knowledge Nikhil Kandpal H. Deng Adam Roberts Eric Wallace Colin Raffel RALM KELM 71 409 0 15 Nov 2022
Locating and Editing Factual Associations in GPT Kevin Meng David Bau A. Andonian Yonatan Belinkov KELM 119 1,275 0 10 Feb 2022
Language Models are Few-Shot Learners Tom B. Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared Kaplan ... Christopher Berner Sam McCandlish Alec Radford Ilya Sutskever Dario Amodei BDL 383 41,106 0 28 May 2020
Language Models as Knowledge Bases? Fabio Petroni Tim Rocktaschel Patrick Lewis A. Bakhtin Yuxiang Wu Alexander H. Miller Sebastian Riedel KELM AI4MH 515 2,639 0 03 Sep 2019
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks Nils Reimers Iryna Gurevych 513 11,979 0 27 Aug 2019