A Unified Approach to Interpreting Knowledge Distillation for Large Language Models via Interactions
In this paper, we propose a unified approach to explore the common mechanism of various KD methods using interactions. Specifically, we decompose the output score of the LLM into the sum of numerous interactions. Each interaction represents a nonlinear relationship involving a set of input variables (e.g., words).
- ▪In this paper, we propose a unified approach to explore the common mechanism of various KD methods using interactions.
- ▪Specifically, we decompose the output score of the LLM into the sum of numerous interactions.
- ▪Each interaction represents a nonlinear relationship involving a set of input variables (e.g., words).
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2607.08776 |
| Publication time | Mon, 13 Jul 2026 00:00:00 -0400 |
| Retrieval time | 2026-07-13T04:20:37.625Z |
| Last seen | 2026-07-13T04:20:37.625Z |
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| Summary source text | contentText |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Machine Learning arXiv:2607.08776 (cs) [Submitted on 5 May 2026] Title:A Unified Approach to Interpreting Knowledge Distillation for Large Language Models via Interactions Authors:Qingzhuo Wang, Ruiyang Qin, Zhenxin Qin, Wen Shen, Zhihua Wei View a PDF of the paper titled A Unified Approach to Interpreting Knowledge Distillation for Large Language Models via Interactions, by Qingzhuo Wang and 4 other authors View PDF HTML (experimental) Abstract:Despite the success of knowledge distillation (KD) in Large Language Models (LLMs), the underlying mechanism behind its efficacy remains unclear. In this paper, we propose a unified approach to explore the common mechanism of various KD methods using interactions.
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