This AI article reveals: How large language models compare to search engines in fact-checking efficiency

Researchers from different universities compare the effectiveness of language models (LLM) and search engines to assist in data verification. LLM explanations help users verify facts more efficiently than search engines, but users tend to trust LLMs even when the explanations are incorrect. Adding contrasting information reduces over-reliance, but only significantly outperforms search engines. In high-stakes situations, LLM explanations may not be a reliable substitute for reading retrieved passages, as relying on incorrect ai explanations could have serious consequences.

His research compares language models and search engines for fact-checking and finds that language model explanations improve efficiency but can 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, which takes less time but discourages internet searches for claims.

The current study focuses on the role of LLMs in fact-checking and their efficiency compared to search engines. LLM explanations are more effective but lead to overconfidence, especially when they are incorrect. Contrasting explanations are proposed but do not 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 linguistic models and search engines in data verification using 80 collaborative workers. Language model explanations improve efficiency, but users tend to rely on them too much. 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 the impact of retrieval and explanation.

Language model explanations improve fact-checking accuracy compared to a no-evidence baseline. Recovered passages also improve accuracy. There is no significant difference in accuracy between the language model explanations and the retrieved passages, but the explanations are read faster. It does not surpass recall in precision. Linguistic models can convincingly explain incorrect statements, which could lead to erroneous judgments. LLM explanations may not replace reading passages, especially in high-stakes situations.

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

The study proposes to personalize evidence for users, strategically combine retrieval and explanation, and explore 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 linguistic model explanations, especially in high-stakes situations. Explores methods to improve the reliability and accuracy of these explanations as a viable alternative to reading recovered passages.

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