CAREER: Explanation-based Optimization of Diversified Information Retrieval to Enhance AI Systems

大型生成性人工智能(AI)模型，如ChatGPT，被广泛用于信息搜索-帮助人们找到关于某个主题的信息。与传统搜索引擎相比，它们提供了一个连贯的叙述，这可能会促进用户搜索的探索阶段。生成性人工智能反应更具可读性、连贯性和上下文适宜性；因此，它们听起来更权威和明确。然而，现有的生成性人工智能模型存在幻觉、不受支持的误导性答案、明显的错误信息和隐藏的偏见等问题。另一个问题是，大多数用户查询都是模棱两可的。目前的系统，包括那些使用生成性人工智能模型的系统，没有通过为用户的查询提供替代答案来适当地考虑模糊性。该项目的愿景是使用户能够使用生成性人工智能模型来获得信息访问中各种问题或任务所需的可解释、多样化和不偏不倚的备选答案、观点、副主题或方面的集合，其中每个不同的答案或观点都忠实地归因于一组证据和支持信息来源。该项目旨在使用户更容易、更有效、更值得信赖地获取信息。鉴于搜索是最常见的在线活动之一，该项目的定位是对社会产生重大影响，促进对主题的更全面理解，鼓励批判性思维，并促进明智的决策。为了实现上述目标，该项目建议开发新的检索模式，以提高其结果的相关性、多样性和可解释性。该项目将开发搜索结果多粒度多样化的模型，以显著提高检索模型在为开放领域查询提供不同结果方面的普适性。此外，该项目通过解释搜索结果的相关性和多样性，使人工智能系统能够充分利用搜索结果。该项目建立在可解释搜索结果的基础上，引入了基于解释的搜索结果优化。这涉及到基于对检索模型故障的推理来改进搜索结果。由此产生的检索系统将特别有助于通过访问可解释的显式知识来增强大型生成性人工智能模型。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Large generative artificial intelligence (AI) models, such as ChatGPT, are widely used for information seeking—helping people find information on a topic. Compared to traditional search engines, they provide a coherent narrative, which could potentially facilitate the exploratory phase of users' searches. Generative AI responses are more readable, coherent, and contextually appropriate; hence, they sound authoritative and definitive. However, existing generative AI models are subject to problems such as hallucinations, unsupported misleading answers, outright misinformation, and hidden biases. Another issue is that the majority of user queries are ambiguous. Current systems, including those that employ generative AI models, do not appropriately consider ambiguity by providing users with alternative answers to their queries. The vision of this project is to enable users to use generative AI models to obtain an interpretable, diverse, and unbiased set of alternative answers, viewpoints, subtopics, or aspects as required for various questions or tasks in information access, where each distinct answer or viewpoint is faithfully attributable to a set of evidence and supporting information sources. This project aims to make information access easier, more effective, and more trustworthy for users. Given that search is among the most common online activities, this project is positioned to have a substantial impact on society, promoting a more comprehensive understanding of topics, encouraging critical thinking, and facilitating informed decision-making.To achieve the above goal, this project proposes the development of novel retrieval models to enhance the relevance, diversity, and interpretability of their results. This project will develop models for multi-granular diversification of search results to significantly improve the generalizability of retrieval models in providing diverse results for open-domain queries. In addition, this project enables the full utilization of search results by AI systems through explanations of their relevance and diversity. Building on top of explainable search results, the project introduces explanation-based optimization of search results. This involves improving search results based on reasoning over failures of retrieval models. The resulting retrieval systems will be particularly useful for augmenting large generative AI models through access to explainable explicit knowledge.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.