I've got the "Answer"! Interpretation of LLMs Hidden States in Question Answering

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Interpretability and explainability of AI are becoming increasingly important in light of the rapid development of large language models (LLMs).

• This paper investigates the interpretation of LLMs in the context of the knowledge-based question answering.
• The main hypothesis of the study is that correct and incorrect model behavior can be distinguished at the level of hidden states.
• The quantized models LLaMA-2-7B-Chat, Mistral-7B, Vicuna-7B and the MuSeRC question-answering dataset are used to test this hypothesis.