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Large Language Models (LLMs) are increasingly used for accessing information on the web. Their truthfulness and factuality are thus of great interest. To help users make the right decisions about the information they get, LLMs should not only provide information but also help users fact-check it. Our experiments with 80 crowdworkers compare language models with search engines (information retrieval systems) at facilitating fact-checking. We prompt LLMs to validate a given claim and provide corresponding explanations. Users reading LLM explanations are significantly more efficient than those using search engines while achieving similar accuracy. However, they over-rely on the LLMs when the explanation is wrong. To reduce over-reliance on LLMs, we ask LLMs to provide contrastive information - explain both why the claim is true and false, and then we present both sides of the explanation to users. This contrastive explanation mitigates users' over-reliance on LLMs, but cannot significantly outperform search engines. Further, showing both search engine results and LLM explanations offers no complementary benefits compared to search engines alone. Taken together, our study highlights that natural language explanations by LLMs may not be a reliable replacement for reading the retrieved passages, especially in high-stakes settings where over-relying on wrong AI explanations could lead to critical consequences.
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Large Language Models and Future of Information Retrieval: Opportunities and Challenges
Recent years have seen great success of large language models (LLMs) in performing many natural language processing tasks with impressive performance, including tasks that directly serve users such as question answering and text summarization. They open up unprecedented opportunities for transforming information retrieval (IR) research and applications. However, concerns such as halluciation undermine their trustworthiness, limiting their actual utility when deployed in real-world applications, especially high-stake applications where trust is vital. How can we both exploit the strengths of LLMs and mitigate any risk caused by their weaknesses when applying LLMs to IR? What are the best opportunities for us to apply LLMs to IR? What are the major challenges that we will need to address in the future to fully exploit such opportunities? Given the anticipated growth of LLMs, what will future information retrieval systems look like? Will LLMs eventually replace an IR system? In this perspective paper, we examine these questions and provide provisional answers to them. We argue that LLMs will not be able to replace search engines, and future LLMs would need to learn how to use a search engine so that they can interact with a search engine on behalf of users. We conclude with a set of promising future research directions in applying LLMs to IR.
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- Award ID(s):
- 2229612
- PAR ID:
- 10591736
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400704314
- Page Range / eLocation ID:
- 481 to 490
- Format(s):
- Medium: X
- Location:
- Washington DC USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3626772.3657848
