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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.19116 |
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| _version_ | 1866911226075807744 |
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| author | Bae, Jaesung Churchwell, Cameron Hermon, Mitchell Hsieh, Tsun-An Xu, Jocelyn Yegorova, Yekaterina Hasegawa-Johnson, Mark Ji, Heng |
| author_facet | Bae, Jaesung Churchwell, Cameron Hermon, Mitchell Hsieh, Tsun-An Xu, Jocelyn Yegorova, Yekaterina Hasegawa-Johnson, Mark Ji, Heng |
| contents | This paper investigates how large language models (LLMs) behave when faced with discrepancies between their parametric knowledge and conflicting information contained in a prompt. Building on prior question-answering (QA) research, we extend the investigation of knowledge conflicts to the realm of code generation. We propose a domain-agnostic framework for constructing and interpreting such conflicts, along with a novel evaluation method and dataset tailored to code conflict scenarios. Our experiments indicate that sufficiently large LLMs encode the notion of a knowledge conflict in their parameters, enabling us to detect knowledge conflicts with up to \textbf{80.65\%} accuracy. Building on these insights, we show that activation-level steering can achieve up to a \textbf{12.6\%} improvement in steering success over a random baseline. However, effectiveness depends critically on balancing model size, task domain, and steering direction. The experiment code and data will be made publicly available after acceptance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_19116 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | That's Deprecated! Understanding, Detecting, and Steering Knowledge Conflicts in Language Models for Code Generation Bae, Jaesung Churchwell, Cameron Hermon, Mitchell Hsieh, Tsun-An Xu, Jocelyn Yegorova, Yekaterina Hasegawa-Johnson, Mark Ji, Heng Computation and Language Artificial Intelligence Machine Learning This paper investigates how large language models (LLMs) behave when faced with discrepancies between their parametric knowledge and conflicting information contained in a prompt. Building on prior question-answering (QA) research, we extend the investigation of knowledge conflicts to the realm of code generation. We propose a domain-agnostic framework for constructing and interpreting such conflicts, along with a novel evaluation method and dataset tailored to code conflict scenarios. Our experiments indicate that sufficiently large LLMs encode the notion of a knowledge conflict in their parameters, enabling us to detect knowledge conflicts with up to \textbf{80.65\%} accuracy. Building on these insights, we show that activation-level steering can achieve up to a \textbf{12.6\%} improvement in steering success over a random baseline. However, effectiveness depends critically on balancing model size, task domain, and steering direction. The experiment code and data will be made publicly available after acceptance. |
| title | That's Deprecated! Understanding, Detecting, and Steering Knowledge Conflicts in Language Models for Code Generation |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2510.19116 |