Researchers from Bar Ilan University, Google Research, Google DeepMind, and Tel Aviv University have developed REVEAL, a benchmark dataset for evaluating automatic verifiers of complex reasoning in open-domain question answering. It covers 704 questions and focuses on logical correctness and attribution to evidence passages in language models’ answers, highlighting the need for fine-grained datasets to improve reasoning chains. The study emphasizes the challenges and opportunities in this area, offering avenues for improvement.
Introducing REVEAL: A Game-Changing Dataset for Evaluating AI Reasoning
Researchers from Bar Ilan University, Google Research, Google DeepMind, and Tel Aviv University have collaborated to develop REVEAL, a groundbreaking benchmark dataset for assessing automatic verifiers of complex reasoning in open-domain question answering.
The dataset provides comprehensive labels for relevance, attribution to evidence passages, and logical correctness of each reasoning step in language models’ answers, addressing the need for fine-grained step-level datasets in this area. It covers 704 questions from popular QA datasets and 1,002 CoT answers generated by three language models, evaluating the performance of state-of-the-art language models in this context.
The study emphasizes the challenges and opportunities for current verifiers, highlighting the need for improvement in state-of-the-art solutions. It also offers a verification protocol, annotation schema, and detailed analyses of challenges, shedding light on avenues for improvement in AI reasoning.
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If you want to evolve your company with AI, stay competitive, and use it to your advantage, consider leveraging the groundbreaking REVEAL dataset to benchmark and improve the verification of complex reasoning in language models.
