待翻譯：From Preferences to Principles: Rubric-Based Alignment for Grounded Knowledge Answers
AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Designing effective reward signals for open-domain question answering is challenging because high-quality responses must simultaneously satisfy multiple aspects of answer quality that are difficult to capture with a holistic scalar objective. We introduce a rubric-based reward framework that generates query-specific rubrics grounded in retrieved evidence and decomposed into multiple quality dimensions, providing fine-grained supervision during post-training. Averaged across three evaluation axes (composition, grounding, and instruction-following), our approach improves over the…
AI 服務暫時不可用，以下為來源正文，待恢復後補全翻譯。
content type paperpublished August 2026 From Preferences to Principles: Rubric-Based Alignment for Grounded Knowledge Answers AuthorsAman Saini, Priyanshu Kumar, Eric Peng, Kai Yuan, Harsh Girase, Wanming Chen View publication Designing effective reward signals for open-domain question answering is challenging because high-quality responses must simultaneously satisfy multiple aspects of answer quality that are difficult to capture with a holistic scalar objective. We introduce a rubric-based reward framework that generates query-specific rubrics grounded in retrieved evidence and decomposed into multiple quality dimensions, providing fine-grained supervision during post-training. Averaged across three evaluation axes (composition, grounding, and instruction-following), our approach improves over the instruction-tuned baseline by 6.5% and over flat rubric variants by 4%, with consistent gains across all evaluation datasets. Conditioning rubrics on retrieved evidence improves factual support, while decomposing rubrics into quality-specific dimensions further improves coherence, organization, and adherence to query requirements. Our results show that grounded, multi-dimensional rubrics provide more effective reward supervision for complex open-domain question answering. Can Open Domain Question Answering Models Answer Visual Knowledge Questions? February 28, 2022research area Speech and Natural Language Processing The task of Outside Knowledge Visual Question Answering (OKVQA) requires an automatic system to answer natural language questions about pictures and images using external knowledge. We observe that many visual questions, which contain deictic referential phrases referring to entities in the image, can be rewritten as “non-grounded” questions and can be answered by existing text-based question answering systems. This allows for the reuse of… Read more MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering July 30, 2020research area Knowledge Bases and Search, research area Speech and Natural Language Processing Progress in cross-lingual modeling depends on challenging, realistic, and diverse evaluation sets. We introduce Multilingual Knowledge Questions and Answers (MKQA), an open-domain question answering evaluation set comprising 10k question-answer pairs aligned across 26 typologically diverse languages (260k question-answer pairs in total). The goal of this dataset is to provide a challenging benchmark for question answering quality across a wide… Read more