LLMs Know More Than They Show: On the Intrinsic Representation of LLM Hallucinations (2410.02707v2)

Abstract: LLMs often produce errors, including factual inaccuracies, biases, and reasoning failures, collectively referred to as "hallucinations". Recent studies have demonstrated that LLMs' internal states encode information regarding the truthfulness of their outputs, and that this information can be utilized to detect errors. In this work, we show that the internal representations of LLMs encode much more information about truthfulness than previously recognized. We first discover that the truthfulness information is concentrated in specific tokens, and leveraging this property significantly enhances error detection performance. Yet, we show that such error detectors fail to generalize across datasets, implying that -- contrary to prior claims -- truthfulness encoding is not universal but rather multifaceted. Next, we show that internal representations can also be used for predicting the types of errors the model is likely to make, facilitating the development of tailored mitigation strategies. Lastly, we reveal a discrepancy between LLMs' internal encoding and external behavior: they may encode the correct answer, yet consistently generate an incorrect one. Taken together, these insights deepen our understanding of LLM errors from the model's internal perspective, which can guide future research on enhancing error analysis and mitigation.

Insights into LLMs: Intrinsic Representations and Hallucinations

The paper "LLMs Know More Than They Show: On the Intrinsic Representation of LLM Hallucinations" explores the intrinsic representation of hallucinations in LLMs. The authors focus on understanding the encoding of truthfulness in LLMs' internal states and how this understanding can enhance error detection and mitigation strategies. The research challenges prior assumptions about the universality of truthfulness encoding, revealing task-specific and skill-based nuances.

Overview

The paper advances the understanding of LLMs by examining how their internal representations contain signals related to the truthfulness of generated outputs. Focusing on what are often termed "hallucinations"—factual inaccuracies, biases, or reasoning failures—the researchers propose that truthfulness information is particularly concentrated in specific tokens, particularly those directly related to the exact answer. This insight provides a method to significantly improve error detection accuracy by focusing on these "exact answer tokens."

Key Findings

• Localized Encoding of Truthfulness: The authors reveal that truthfulness signals are not uniformly distributed but concentrated in tokens associated with the exact answer. By leveraging this property, they improve error detection methods.
• Evaluation of Probing Classifiers: The paper utilizes probing classifiers to evaluate internal representations and predict errors. These classifiers perform well in detecting errors by focusing on specific layers and tokens, providing insights into the LLMs' processing mechanisms.
• Task-Specific Generalization: The research examines the generalization of probing classifiers across tasks. The results indicate limited generalization, suggesting that truthfulness signals are task-specific rather than universally encoded, linked to particular skills required by different tasks.
• Error Type Analysis: By resampling model responses, the paper identifies different types of errors within a single task, showing that LLMs have intrinsic knowledge about the types of errors they are likely to generate.
• Discrepancy in Encoding and Behavior: There is often a discrepancy between what LLMs encode internally and their external outputs. This suggests that while LLMs may have the right answer encoded, they might still produce incorrect outputs.

Implications

The implications of these findings are multifaceted:

• Error Detection Enhancement: By targeting exact answer tokens, developers can design more effective error detection strategies. This approach is particularly useful for practical applications where accuracy is paramount.
• Tailored Error Mitigation: Understanding the task-specific nature of truthfulness encoding allows developers to implement task-specific mitigation strategies, improving LLM performance in diverse applications.
• Model Behavior Interpretation: The discrepancy between internal states and external behavior highlights areas for further refinement in model training and deployment, aiming to align internal knowledge with generated content more closely.

Future Directions

Given the insights into how LLMs process and encode truthfulness, future research could focus on:

• Developing More Sophisticated Probing Techniques: Enhancing probing methods to dig deeper into the different layers and representations within LLMs.
• Exploring Fine-Tuning Strategies: Investigating training methods that align internal encoding of truthfulness with external outputs, potentially reducing factual inaccuracies in model generation.
• Evaluating Model Performance Across Diverse Domains: Expanding the scope of tasks to further understand the limits and potential improvements in LLM truthfulness encoding.

In conclusion, this paper provides a rigorous exploration of how LLMs internally process and represent truthfulness. By uncovering the nuanced ways in which models encode and potentially misrepresent information, it lays the groundwork for more reliable and sophisticated AI applications. The findings challenge existing perceptions of universal truthfulness encoding, emphasizing the complexity and task-specific nature of LLM architectures.