This AI Paper Reveals: How Large Language Models Stack Up Against Search Engines in Fact-Checking Efficiency

Researchers from different Universities compare the effectiveness of language models (LLMs) and search engines in aiding fact-checking. LLM explanations help users fact-check more efficiently than search engines, but users tend to rely on LLMs even when the explanations are incorrect. Adding contrastive information reduces over-reliance but only significantly outperforms search engines. In high-stakes situations, LLM explanations may not be a reliable replacement for reading retrieved passages, as relying on incorrect AI explanations could have serious consequences.

Their research compares language models and search engines for fact-checking, finding that language model explanations enhance efficiency but may lead to over-reliance when incorrect. In high-stakes scenarios, LLM explanations may not replace reading passages. Another study shows that ChatGPT explanations improve human verification compared to retrieved passages, taking less time but discouraging internet searches for claims.

The current study focuses on LLMs’ role in fact-checking and their efficiency compared to search engines. LLM explanations are more effective but lead to over-reliance, especially when wrong. Contrastive explanations are proposed but don’t outperform search engines. LLM explanations may not replace reading passages in high-stakes situations, as relying on incorrect AI explanations could have serious consequences.

The proposed method compares language models and search engines in fact-checking using 80 crowdworkers. Language model explanations improve efficiency, but users tend to over-rely on them. It also examines the benefits of combining search engine results with language model explanations. The study uses a between-subjects design, measuring accuracy and verification time to evaluate retrieval and explanation’s impact.

Language model explanations improve fact-checking accuracy compared to a baseline with no evidence. Retrieved passages also enhance accuracy. There’s no significant accuracy difference between language model explanations and retrieved passages, but explanations are faster to read. It doesn’t outperform retrieval in accuracy. Language models can convincingly explain incorrect statements, potentially leading to wrong judgments. LLM explanations may not replace reading passages, especially in high-stakes situations.

In conclusion, LLMs improve fact-checking accuracy but pose the risk of over-reliance and incorrect judgments when their explanations are wrong. Combining LLM explanations with search results offers no additional benefits. LLM explanations are quicker to read but can convincingly explain false statements. In high-stakes situations, relying solely on LLM explanations is not advisable; reading retrieved passages remains crucial for accurate verification.

The study proposes customizing evidence for users, combining retrieval and explanation strategically, and exploring when to show explanations or retrieved passages. It investigates the effects of presenting both simultaneously on verification accuracy. The research also examines the risks of over-reliance on language model explanations, especially in high-stakes situations. It explores methods to enhance the reliability and accuracy of these explanations as a viable alternative to reading retrieved passages.

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