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

Key Points
1. Large language models (LLMs) often produce errors, including factual inaccuracies, biases, and reasoning failures, collectively referred to as "hallucinations".

2. Recent studies have demonstrated that LLMs' internal states encode information regarding the truthfulness of their outputs, and this information can be utilized to detect errors.

3. The authors show that the internal representations of LLMs encode much more information about truthfulness than previously recognized. They find that the truthfulness information is concentrated in specific tokens, and leveraging this property significantly enhances error detection performance.

4. However, the authors show that such error detectors fail to generalize across datasets, implying that truthfulness encoding is not universal but rather multifaceted.

5. The authors 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.

6. The authors reveal a discrepancy between LLMs' internal encoding and external behavior: they may encode the correct answer, yet consistently generate an incorrect one.

7. The authors adopt a broad interpretation of hallucinations, considering them to encompass all errors produced by an LLM, including factual inaccuracies, biases, common-sense reasoning failures, and other real-world errors.

8. The authors focus on long-form generations, which reflect real-world usage of LLMs, and find that truthfulness information is concentrated in the exact answer tokens.

9. The insights from this work deepen the understanding of LLM errors from the model's internal perspective, which can guide future research on enhancing error analysis and mitigation."

Summary

Research Scope and Findings
This research paper examines the internal representations of large language models (LLMs) and their ability to detect errors, known as "hallucinations."

The key findings are: 1. Truthfulness information is concentrated in specific tokens within the LLM's internal representations, especially the exact answer tokens. Leveraging this improves error detection performance significantly. 2. However, the truthfulness encoding is not universal but rather multifaceted, as error detectors based on probing classifiers fail to generalize well across datasets. This indicates that LLMs encode multiple, distinct notions of truth. 3. The internal representations can be used to predict the types of errors the model is likely to make, which can guide the development of tailored mitigation strategies. 4. There is a discrepancy between the LLM's internal encoding and its external behavior - the model may encode the correct answer, yet consistently generate an incorrect one. This suggests that the model's external behavior may misrepresent its actual abilities.

Implications and Future Research
Overall, the findings provide a deeper understanding of LLM errors from the model's internal perspective, which can inform future research on enhancing error analysis and mitigation. The researchers argue that shifting focus from human-centric interpretations of hallucinations to a model-centric approach is crucial for addressing the root causes of LLM errors.

Reference: https://arxiv.org/abs/2410.02707