{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/read-before-generate-faithful-long-form","title":"Read before Generate! Faithful Long Form Question Answering with Machine Reading","arxiv_id":"2203.00343","date":"2022-03-01","proceeding":"Findings (ACL) 2022 5","authors":["Dan Su","Xiaoguang Li","Jindi Zhang","Lifeng Shang","Xin Jiang","Qun Liu","Pascale Fung"],"abstract":"Long-form question answering (LFQA) aims to generate a paragraph-length answer for a given question. While current work on LFQA using large pre-trained model for generation are effective at producing fluent and somewhat relevant content, one primary challenge lies in how to generate a faithful answer that has less hallucinated content. We propose a new end-to-end framework that jointly models answer generation and machine reading. The key idea is to augment the generation model with fine-grained, answer-related salient information which can be viewed as an emphasis on faithful facts. State-of-the-art results on two LFQA datasets, ELI5 and MS MARCO, demonstrate the effectiveness of our method, in comparison with strong baselines on automatic and human evaluation metrics. A detailed analysis further proves the competency of our methods in generating fluent, relevant, and more faithful answers.","url_abs":"https://arxiv.org/abs/2203.00343v1","url_pdf":"https://arxiv.org/pdf/2203.00343v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"answer-generation","task_name":"Answer Generation"},{"task_slug":"form","task_name":"Form"},{"task_slug":"long-form-question-answering","task_name":"Long Form Question Answering"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"reading-comprehension","task_name":"Reading Comprehension"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/question-answering-on-kilt-eli5","task":"Question Answering","dataset":"KILT: ELI5","model":"RBG","rank_in_archive_order":1,"of":7,"metrics":{"F1":"24.53","Rouge-L":"27.13"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2203.00343","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}