Large Language Models Help Humans Verify Truthfulness -- Except When They Are Convincingly Wrong

• URL: http://arxiv.org/abs/2310.12558v2
• Date: Mon, 1 Apr 2024 21:55:06 GMT
• Title: Large Language Models Help Humans Verify Truthfulness -- Except When They Are Convincingly Wrong
• Authors: Chenglei Si, Navita Goyal, Sherry Tongshuang Wu, Chen Zhao, Shi Feng, Hal Daumé III, Jordan Boyd-Graber,
• Abstract summary: Large Language Models (LLMs) are increasingly used for accessing information on the web. Our experiments with 80 crowdworkers compare language models with search engines (information retrieval systems) at facilitating fact-checking. Users reading LLM explanations are significantly more efficient than those using search engines while achieving similar accuracy.
• Score: 35.64962031447787
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• LLM-Generated Black-box Explanations Can Be Adversarially Helpful [16.49758711633611]
  Large Language Models (LLMs) help us solve and understand complex problems by acting as digital assistants. Our research uncovers a hidden risk tied to this approach, which we call *adversarial helpfulness*. This happens when an LLM's explanations make a wrong answer look right, potentially leading people to trust incorrect solutions.
  arXiv  Detail & Related papers  (2024-05-10T20:23:46Z)
• Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When and What to Retrieve for LLMs [60.40396361115776]
  This paper introduces a novel collaborative approach, namely SlimPLM, that detects missing knowledge in large language models (LLMs) with a slim proxy model. We employ a proxy model which has far fewer parameters, and take its answers as answers. Heuristic answers are then utilized to predict the knowledge required to answer the user question, as well as the known and unknown knowledge within the LLM.
  arXiv  Detail & Related papers  (2024-02-19T11:11:08Z)
• When Do LLMs Need Retrieval Augmentation? Mitigating LLMs' Overconfidence Helps Retrieval Augmentation [66.01754585188739]
  Large Language Models (LLMs) have been found to have difficulty knowing they do not possess certain knowledge. Retrieval Augmentation (RA) has been extensively studied to mitigate LLMs' hallucinations. We propose several methods to enhance LLMs' perception of knowledge boundaries and show that they are effective in reducing overconfidence.
  arXiv  Detail & Related papers  (2024-02-18T04:57:19Z)
• Rethinking Interpretability in the Era of Large Language Models [76.1947554386879]
  Large language models (LLMs) have demonstrated remarkable capabilities across a wide array of tasks. The capability to explain in natural language allows LLMs to expand the scale and complexity of patterns that can be given to a human. These new capabilities raise new challenges, such as hallucinated explanations and immense computational costs.
  arXiv  Detail & Related papers  (2024-01-30T17:38:54Z)
• See the Unseen: Better Context-Consistent Knowledge-Editing by Noises [73.54237379082795]
  Knowledge-editing updates knowledge of large language models (LLMs) Existing works ignore this property and the editing lacks generalization. We empirically find that the effects of different contexts upon LLMs in recalling the same knowledge follow a Gaussian-like distribution.
  arXiv  Detail & Related papers  (2024-01-15T09:09:14Z)
• Supervised Knowledge Makes Large Language Models Better In-context Learners [94.89301696512776]
  Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering. The challenge of improving the generalizability and factuality of LLMs in natural language understanding and question answering remains under-explored. We propose a framework that enhances the reliability of LLMs as it: 1) generalizes out-of-distribution data, 2) elucidates how LLMs benefit from discriminative models, and 3) minimizes hallucinations in generative tasks.
  arXiv  Detail & Related papers  (2023-12-26T07:24:46Z)
• Are Large Language Models Temporally Grounded? [38.481606493496514]
  We provide Large language models (LLMs) with textual narratives. We probe them with respect to their common-sense knowledge of the structure and duration of events. We evaluate state-of-the-art LLMs on three tasks reflecting these abilities.
  arXiv  Detail & Related papers  (2023-11-14T18:57:15Z)
• LLatrieval: LLM-Verified Retrieval for Verifiable Generation [67.93134176912477]
  Verifiable generation aims to let the large language model (LLM) generate text with supporting documents. We propose LLatrieval (Large Language Model Verified Retrieval), where the LLM updates the retrieval result until it verifies that the retrieved documents can sufficiently support answering the question. Experiments show that LLatrieval significantly outperforms extensive baselines and achieves state-of-the-art results.
  arXiv  Detail & Related papers  (2023-11-14T01:38:02Z)
• Knowing What LLMs DO NOT Know: A Simple Yet Effective Self-Detection Method [36.24876571343749]
  Large Language Models (LLMs) have shown great potential in Natural Language Processing (NLP) tasks. Recent literature reveals that LLMs generate nonfactual responses intermittently. We propose a novel self-detection method to detect which questions that a LLM does not know that are prone to generate nonfactual results.
  arXiv  Detail & Related papers  (2023-10-27T06:22:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.