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

大型生成式人工智能（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。与传统的搜索引擎相比，它们提供了一个连贯的叙述，这可能有助于用户搜索的探索阶段。生成式AI响应更具可读性，连贯性和上下文适当性;因此，它们听起来权威和明确。然而，现有的生成式人工智能模型存在诸如幻觉、不支持的误导性答案、彻底的错误信息和隐藏的偏见等问题。另一个问题是，大多数用户查询是模糊的。当前的系统，包括那些采用生成式人工智能模型的系统，没有通过为用户提供查询的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式AI模型来获得可解释的，多样化的，无偏见的一组替代答案，观点，子主题或信息访问中各种问题或任务所需的方面，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源。该项目旨在使信息访问更容易，更有效，更值得用户信赖。鉴于搜索是最常见的在线活动之一，本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，本项目提出开发新的检索模型，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，以显着提高检索模型的通用性，为开放域查询提供多样化的结果。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。在可解释的搜索结果的基础上，该项目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失败的推理来改进搜索结果。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值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.