Key Points
1. The paper introduces a new framework called Patchscopes, which aims to inspect the hidden representations of large language models to understand their behavior and verify their alignment with human values.
2. The proposed Patchscopes framework leverages the model's advanced capabilities in generating human-like text to decode specific information from the hidden representations in a natural language format, allowing for a wide range of questions to be answered.
3. The framework unifies and extends prior interpretability methods, providing more robust and expressive alternatives that mitigate their limitations and introduce new inspection possibilities.
4. Experimental results demonstrate the effectiveness of Patchscopes in tasks such as estimating next-token predictions, extracting specific attributes from entity representations, analyzing entity resolution in early layers, and enhancing multi-hop reasoning by self-correction.
5. The Patchscopes were compared with traditional methods such as linear probing and vocabulary projections, and consistently outperformed them in various tasks, showcasing the effectiveness and versatility of the framework.
6. The paper also discusses the potential of using Patchscopes for practical applications, such as correcting multi-hop reasoning errors and understanding the contextualization process in early layers of language models.
7. Cross-model patching experiments with different sizes of language models demonstrate the potential of leveraging more expressive models to improve the inspection of representations from smaller models.
8. The results indicate that Patchscopes offer a novel and powerful approach to inspecting and understanding the internal representations of large language models, with significant implications for interpretability and model behavior analysis.
9. Overall, the paper makes significant contributions by proposing a unified and versatile framework for inspecting hidden representations in language models, addressing various limitations of existing methods and opening up new avenues for practical use in language model research.
Summary
The paper introduces a new framework called Patchscopes to inspect hidden representations of large language models (LLMs), addressing the limitations of existing interpretability methods. The authors discuss the shortcomings of current methods, such as supervised training, vocabulary projection accuracy, and lack of expressiveness, and present Patchscopes as a more effective and versatile alternative.
They conducted experiments using Patchscopes, showing improved performance in tasks such as next-token prediction, attribute decoding, and analyzing entity resolution processes in early layers of LLMs. The paper also highlights the practical applications of Patchscopes in fixing latent multi-hop reasoning errors. The authors demonstrate the efficacy of Patchscopes in solving multi-hop reasoning tasks where the model fails to process the connection between different reasoning steps, ultimately correcting the final prediction.
These findings suggest that Patchscopes provide a promising framework for inspecting and improving the interpretability of LLMs.
Reference: Asma Ghandeharioun, Avi Caciularu, Adam Pearce, Lucas Dixon, Mor Geva