This is the official code release accompanying our paper"Long-form factuality in large language models". This repository contains: LongFact: A prompt set of 2,280 fact-seeking prompts requiring long-form responses. Search-Augmented Factuality Evaluator (SAFE): Automatic evaluation of model responses...
Large language models have demonstrated significant potential as the next-generation information access engines. However, their reliability is hindered by issues of hallucination and generating non-factual content. This is particularly problematic in long-form responses, where assessing and ensuring factual ...
Long-form factuality in large language models. Jerry Wei, Chengrun Yang, Xinying Song, Yifeng Lu, Nathan Hu, Dustin Tran, Daiyi Peng, Ruibo Liu, Da Huang, Cosmo Du, Quoc V. Le. Arxiv 2024. LUQ: Long-text Uncertainty Quantification for LLMs. JCaiqi Zhang, Fangyu Liu, Marco Basaldell...
LongForm: Effective Instruction Tuning with Reverse Instructions akoksal/longform • 17 Apr 2023 We generate instructions via LLMs for human-written corpus examples using reverse instructions. 1 Paper Code Search-in-the-Chain: Interactively Enhancing Large Language Models with Search for Knowledge-...
Large language models (LLMs) often suffer from hallucinations, posing significant challenges for real-world applications. Confidence calibration, which estimates the underlying uncertainty of model predictions, is essential to enhance the LLMs' trustworthiness. Existing research on LLM calibration has primar...
This is a repository for OLAPH: Improving Factuality in Biomedical Long-form Question Answering by Minbyul Jeong, Hyeon Hwang, Chanwoong Yoon, Taewhoo Lee, and Jaewoo Kang.MedLFQA | Self-BioRAG (OLAPH) | BioMistral (OLAPH) | Mistral (OLAPH) | Summary | PaperMe...