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

职业：基于解释的多样化信息检索优化以增强人工智能系统

• 批准号: 2339932
• 负责人: Razieh Rahimi
• 金额: $ 59.99万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-09-01 至 2029-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2339932&HistoricalAwards=false
• 关键词: CAREER; Explanation; based; Optimization; Diversified

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.

ChatGPT 等大型生成人工智能 (AI) 模型广泛用于信息搜索，帮助人们查找某个主题的信息，与传统搜索引擎相比，它们提供了连贯的叙述，这可能会促进用户的探索阶段。生成式人工智能的回答更具可读性、连贯性和语境恰当性；因此，它们听起来具有权威性和明确性。然而，现有的生成式人工智能模型存在诸如幻觉、缺乏支持的误导性答案、完全错误的信息和隐藏等问题。另一个问题是，大多数用户查询都是模糊的，包括那些采用生成式人工智能模型的系统，没有通过为用户的查询提供替代答案来适当地考虑歧义。使用生成式人工智能模型来获取信息访问中各种问题或任务所需的一组可解释的、多样化的和公正的替代答案、观点、子主题或方面，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该项目旨在让用户更轻松、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，该项目旨在对社会产生重大影响，促进对信息的更全面的了解。为了实现上述目标，该项目提出开发新颖的检索模型，以增强其结果的相关性、多样性和可解释性。该项目将开发多粒度模型。搜索结果多样化，显着提高检索模型在为开放域查询提供不同结果方面的通用性此外，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。基于搜索结果的优化。这涉及基于对检索模型失败的推理来改进搜索结果，这对于通过访问可解释的显性知识来增强大型生成人工智能模型特别有用。该奖项反映了 NSF 的法定规定。使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。