Summary of Llms Know More Than They Show: on the Intrinsic Representation Of Llm Hallucinations, by Hadas Orgad et al.
LLMs Know More Than They Show: On the Intrinsic Representation of LLM Hallucinations
by Hadas Orgad, Michael Toker, Zorik Gekhman, Roi Reichart, Idan Szpektor, Hadas Kotek, Yonatan Belinkov
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study delves into the inner workings of large language models (LLMs) to uncover the mechanisms behind their factual inaccuracies, biases, and reasoning failures. Researchers have previously demonstrated that LLMs’ internal states encode information regarding the truthfulness of their outputs, which can be leveraged for error detection. This paper reveals that these internal representations contain more information about truthfulness than initially recognized, with specific tokens holding the key to enhanced error detection performance. However, the study also finds that these detectors struggle to generalize across datasets, suggesting that truthfulness encoding is multifaceted rather than universal. Furthermore, the research shows that LLMs’ internal representations can be used to predict the types of errors they are likely to make, facilitating the development of tailored mitigation strategies. The findings also highlight a discrepancy between LLMs’ internal encoding and external behavior, where they may encode the correct answer but consistently generate an incorrect one. Overall, this study provides valuable insights into LLM errors from the model’s internal perspective, guiding future research on error analysis and mitigation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LLMs often make mistakes, like saying things that aren’t true or being biased. Researchers have found that these models can tell when they’re making a mistake, which is helpful for detecting errors. This study takes it further by showing that the model’s internal thoughts contain even more information about whether something is true or not. The team discovered that certain words are especially important for figuring out if the model is telling the truth. However, they also found that these detectors don’t work well across different datasets, which means the way the model thinks about truthfulness can be different depending on what it’s looking at. The researchers were able to use this information to predict what kind of mistakes the model might make and develop strategies to fix them. Interestingly, the study shows that even when the model knows the right answer, it might still give an incorrect response. Overall, this research helps us understand how LLMs work and makes it possible to improve their ability to detect and correct errors. |
