I've got the "Answer"! Interpretation of LLMs Hidden States in Question Answering
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866914822092750848 |
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| author | Goloviznina, Valeriya Kotelnikov, Evgeny |
| author_facet | Goloviznina, Valeriya Kotelnikov, Evgeny |
| contents | 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. The results of the analysis support the proposed hypothesis. We also identify the layers which have a negative effect on the model's behavior. As a prospect of practical application of the hypothesis, we propose to train such "weak" layers additionally in order to improve the quality of the task solution. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_02060 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | I've got the "Answer"! Interpretation of LLMs Hidden States in Question Answering Goloviznina, Valeriya Kotelnikov, Evgeny Computation and Language Artificial Intelligence 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. The results of the analysis support the proposed hypothesis. We also identify the layers which have a negative effect on the model's behavior. As a prospect of practical application of the hypothesis, we propose to train such "weak" layers additionally in order to improve the quality of the task solution. |
| title | I've got the "Answer"! Interpretation of LLMs Hidden States in Question Answering |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2406.02060 |