FoRAG: Factuality-optimized Retrieval Augmented Generation for Web-enhanced Long-form Question Answering

• URL: http://arxiv.org/abs/2406.13779v1
• Date: Wed, 19 Jun 2024 19:06:36 GMT
• Title: FoRAG: Factuality-optimized Retrieval Augmented Generation for Web-enhanced Long-form Question Answering
• Authors: Tianchi Cai, Zhiwen Tan, Xierui Song, Tao Sun, Jiyan Jiang, Yunqi Xu, Yinger Zhang, Jinjie Gu,
• Abstract summary: We propose a novel outline-enhanced generator to achieve clear logic in the generation of multifaceted answers. Then we propose a factuality optimization method based on a carefully designed doubly fine-grained RLHF framework. In particular, when applying our method to Llama2-7B-chat, the derived model FoRAG-L-7B outperforms WebGPT-175B in terms of three commonly used metrics.
• Score: 11.73887020240588
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Retrieval Augmented Generation (RAG) has become prevalent in question-answering (QA) tasks due to its ability of utilizing search engine to enhance the quality of long-form question-answering (LFQA). Despite the emergence of various open source methods and web-enhanced commercial systems such as Bing Chat, two critical problems remain unsolved, i.e., the lack of factuality and clear logic in the generated long-form answers. In this paper, we remedy these issues via a systematic study on answer generation in web-enhanced LFQA. Specifically, we first propose a novel outline-enhanced generator to achieve clear logic in the generation of multifaceted answers and construct two datasets accordingly. Then we propose a factuality optimization method based on a carefully designed doubly fine-grained RLHF framework, which contains automatic evaluation and reward modeling in different levels of granularity. Our generic framework comprises conventional fine-grained RLHF methods as special cases. Extensive experiments verify the superiority of our proposed \textit{Factuality-optimized RAG (FoRAG)} method on both English and Chinese benchmarks. In particular, when applying our method to Llama2-7B-chat, the derived model FoRAG-L-7B outperforms WebGPT-175B in terms of three commonly used metrics (i.e., coherence, helpfulness, and factuality), while the number of parameters is much smaller (only 1/24 of that of WebGPT-175B). Our datasets and models are made publicly available for better reproducibility: https://huggingface.co/forag.

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