SELF-[IN]CORRECT: LLMs Struggle with Discriminating Self-Generated Responses
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909307167047680 |
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| author | Jiang, Dongwei Zhang, Jingyu Weller, Orion Weir, Nathaniel Van Durme, Benjamin Khashabi, Daniel |
| author_facet | Jiang, Dongwei Zhang, Jingyu Weller, Orion Weir, Nathaniel Van Durme, Benjamin Khashabi, Daniel |
| contents | Can LLMs consistently improve their previous outputs for better results? For this to be true, LLMs would need to be better at discriminating among previously-generated alternatives, than generating initial responses. We explore the validity of this hypothesis in practice. We first formulate a unified framework that allows us to compare the generative and discriminative capability of any model on any task. In our resulting experimental analysis of several open-source and industrial LLMs, we observe that models are not reliably better at discriminating among previously-generated alternatives than generating initial responses. This finding challenges the notion that LLMs may be able to enhance their performance only through their own judgment. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2404_04298 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SELF-[IN]CORRECT: LLMs Struggle with Discriminating Self-Generated Responses Jiang, Dongwei Zhang, Jingyu Weller, Orion Weir, Nathaniel Van Durme, Benjamin Khashabi, Daniel Artificial Intelligence Computation and Language Machine Learning Can LLMs consistently improve their previous outputs for better results? For this to be true, LLMs would need to be better at discriminating among previously-generated alternatives, than generating initial responses. We explore the validity of this hypothesis in practice. We first formulate a unified framework that allows us to compare the generative and discriminative capability of any model on any task. In our resulting experimental analysis of several open-source and industrial LLMs, we observe that models are not reliably better at discriminating among previously-generated alternatives than generating initial responses. This finding challenges the notion that LLMs may be able to enhance their performance only through their own judgment. |
| title | SELF-[IN]CORRECT: LLMs Struggle with Discriminating Self-Generated Responses |
| topic | Artificial Intelligence Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2404.04298 |