Скачать с ютуб [2024 Best AI Paper] Patchscopes: A Unifying Framework for Inspecting Hidden Representations of Lang в хорошем качестве

[2024 Best AI Paper] Patchscopes: A Unifying Framework for Inspecting Hidden Representations of Lang

This video was created using https://paperspeech.com. If you’d like to create explainer videos for your own papers, please visit the website! Title: Patchscopes: A Unifying Framework for Inspecting Hidden Representations of Language Models Authors: Asma Ghandeharioun, Avi Caciularu, Adam Pearce, Lucas Dixon, Mor Geva Abstract: Understanding the internal representations of large language models (LLMs) can help explain models' behavior and verify their alignment with human values. Given the capabilities of LLMs in generating human-understandable text, we propose leveraging the model itself to explain its internal representations in natural language. We introduce a framework called Patchscopes and show how it can be used to answer a wide range of questions about an LLM's computation. We show that many prior interpretability methods based on projecting representations into the vocabulary space and intervening on the LLM computation can be viewed as instances of this framework. Moreover, several of their shortcomings such as failure in inspecting early layers or lack of expressivity can be mitigated by Patchscopes. Beyond unifying prior inspection techniques, Patchscopes also opens up new possibilities such as using a more capable model to explain the representations of a smaller model, and multihop reasoning error correction.