HAGRID: A Human-LLM Collaborative Dataset for Generative Information-Seeking with Attribution

• URL: http://arxiv.org/abs/2307.16883v1
• Date: Mon, 31 Jul 2023 17:49:18 GMT
• Title: HAGRID: A Human-LLM Collaborative Dataset for Generative Information-Seeking with Attribution
• Authors: Ehsan Kamalloo, Aref Jafari, Xinyu Zhang, Nandan Thakur, Jimmy Lin
• Abstract summary: We introduce HAGRID (Human-in-the-loop Attributable Generative Retrieval for Information-seeking dataset) for building end-to-end generative information-seeking models. Unlike recent efforts that focus on black-box proprietary search engines, we built our dataset atop the English subset of MIRACL, a publicly available information retrieval dataset.
• Score: 46.41448772928026
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rise of large language models (LLMs) had a transformative impact on search, ushering in a new era of search engines that are capable of generating search results in natural language text, imbued with citations for supporting sources. Building generative information-seeking models demands openly accessible datasets, which currently remain lacking. In this paper, we introduce a new dataset, HAGRID (Human-in-the-loop Attributable Generative Retrieval for Information-seeking Dataset) for building end-to-end generative information-seeking models that are capable of retrieving candidate quotes and generating attributed explanations. Unlike recent efforts that focus on human evaluation of black-box proprietary search engines, we built our dataset atop the English subset of MIRACL, a publicly available information retrieval dataset. HAGRID is constructed based on human and LLM collaboration. We first automatically collect attributed explanations that follow an in-context citation style using an LLM, i.e. GPT-3.5. Next, we ask human annotators to evaluate the LLM explanations based on two criteria: informativeness and attributability. HAGRID serves as a catalyst for the development of information-seeking models with better attribution capabilities.

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