Large Language Models Can Mislead Humans When Providing Incorrect Explanations for Claims

Core Concepts

Large language models can provide convincing but incorrect explanations that lead humans to over-rely on them, resulting in lower accuracy compared to just showing retrieved evidence.

Abstract

The study examines the effectiveness of using large language models (LLMs) like ChatGPT to assist humans in verifying the truthfulness of claims. It compares three main approaches: Retrieval: Showing users the top 10 most relevant passages retrieved from Wikipedia. Explanation: Showing users the explanation generated by ChatGPT on whether the claim is true or false. Contrastive Explanation: Showing users both ChatGPT's supporting and refuting arguments for the claim. The key findings are: Showing either retrieved passages or ChatGPT explanations significantly improves human accuracy compared to the baseline of just showing the claim. However, humans tend to over-rely on ChatGPT explanations, achieving much lower accuracy when the explanations are wrong compared to when they are correct. Contrastive explanations help mitigate this over-reliance, but do not significantly outperform just showing the retrieved passages. Combining retrieval and explanation does not offer complementary benefits over retrieval alone. The study highlights the danger of over-reliance on LLM explanations, especially in high-stakes settings where incorrect explanations could lead to critical consequences. It suggests that while LLMs can be helpful, directly reading the retrieved evidence remains more reliable for human fact-checking than solely relying on the models' natural language explanations.

Stats

Only one spacecraft, Voyager 2, has visited Neptune. Neptune has a total of 14 known moons.

Quotes

"Showing ChatGPT explanation improves human accuracy. When showing explanations to users, the accuracy is μ = 0.74 ± σ = 0.09 compared to the baseline condition where claims are shown without any additional evidence (0.59 ± 0.12)." "When the explanation is correct, users' accuracy is (0.87 ± 0.13), higher than the baseline of having no evidence (0.61 ± 0.13) as well as the retrieval condition (0.79 ± 0.15). However, when the explanation is wrong, users tend to over-trust the explanations and only achieve an accuracy of (0.35 ± 0.22) as compared to the baseline condition (0.49 ± 0.24) and the retrieval condition (0.54 ± 0.26)."

Key Insights Distilled From

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

by Chen... at arxiv.org 04-03-2024

https://arxiv.org/pdf/2310.12558.pdf

Deeper Inquiries

How can we design LLM-based explanations that are more transparent about their limitations and uncertainty to reduce over-reliance?

To design LLM-based explanations that are more transparent about their limitations and uncertainty, several strategies can be implemented: Uncertainty Estimation: Incorporating uncertainty estimation techniques into the LLMs can provide users with a measure of confidence in the generated explanations. This could involve providing a confidence score or range along with the explanation to indicate the model's certainty. Meta-Explanations: Including meta-explanations that detail the model's confidence level, the sources of information used, and the reasoning process can help users understand the basis of the LLM's output. This meta-information can offer insights into the limitations of the model. Highlighting Weaknesses: Explicitly stating the limitations of the model, such as areas where the model may struggle or potential biases, can help users contextualize the explanations and reduce blind trust in the LLM. Interactive Explanations: Providing interactive features that allow users to probe further into the explanation, ask follow-up questions, or request additional information can enhance transparency and help users gauge the reliability of the LLM's output. Visualizations: Using visual aids like heatmaps to show the model's attention or highlighting key parts of the text that influenced the explanation can make the reasoning process more interpretable for users. By implementing these strategies, LLM-based explanations can become more transparent, enabling users to better understand the limitations and uncertainties of the model's outputs and reducing over-reliance on potentially flawed explanations.

What other techniques, beyond contrastive explanations, could help calibrate human trust in LLM outputs for fact-checking tasks?

In addition to contrastive explanations, several techniques can help calibrate human trust in LLM outputs for fact-checking tasks: Explanation Consistency Checks: Implementing a system that checks the consistency of explanations generated by the LLM across different runs or models can help users identify when the model may be providing conflicting or unreliable information. Human-in-the-Loop Verification: Incorporating a human-in-the-loop verification system where human annotators can validate or challenge the LLM's explanations can provide an additional layer of assurance and calibration for users. Explanation Verification: Introducing a mechanism to verify the accuracy of LLM explanations against trusted sources or expert opinions can help users gauge the reliability of the information provided by the model. Explanation Confidence Scores: Assigning confidence scores to LLM explanations based on factors like the model's training data, performance on similar tasks, or the complexity of the query can help users assess the credibility of the explanations. Explanation Auditing: Conducting periodic audits of the LLM's explanations by independent reviewers or fact-checkers can ensure the quality and accuracy of the information presented to users. By incorporating these techniques alongside contrastive explanations, users can have a more nuanced understanding of the LLM outputs and make informed decisions based on a calibrated level of trust.

How might the findings from this study on human-AI collaboration for fact-checking apply to other high-stakes decision-making domains where LLMs are increasingly being deployed?

The findings from this study on human-AI collaboration for fact-checking can have implications for other high-stakes decision-making domains where LLMs are utilized: Medical Diagnosis: In healthcare, LLMs are used for medical diagnosis. Implementing strategies to reduce over-reliance, such as providing transparent explanations and incorporating uncertainty estimation, can help healthcare professionals make more informed decisions based on AI recommendations. Legal Decision-Making: LLMs are employed in legal research and case analysis. Techniques like explanation consistency checks and human-in-the-loop verification can assist legal professionals in verifying the accuracy of AI-generated insights and ensuring the reliability of legal outcomes. Financial Forecasting: LLMs play a role in financial forecasting and risk assessment. By applying explanation verification and explanation confidence scores, financial analysts can better interpret AI-generated predictions and make sound financial decisions. Autonomous Vehicles: In the automotive industry, LLMs are used for autonomous driving systems. Techniques like explanation auditing and interactive explanations can enhance the transparency and reliability of AI decisions in critical situations, improving the safety of autonomous vehicles. By adapting the lessons learned from this study to these high-stakes domains, stakeholders can enhance the collaboration between humans and LLMs, mitigate risks associated with over-reliance, and ensure more trustworthy and effective decision-making processes.