在混合专家语言模型中利用跨语言不一致性进行知识定位
Knowledge Localization in Mixture-of-Experts LLMs Using Cross-Lingual Inconsistency
AI总结：
本文提出利用跨语言不一致性来提高混合专家语言模型的知识定位能力，通过对比成功和失败的激活桶，识别关键专家。实验表明，仅禁用约20个专家，模型在40%的情况下无法正确回答。
摘要：
现代大语言模型在不同语言中表现出显著的行为差异，例如在某些语言中能回忆事实信息而在其他语言中不能。在本文中，我们提出利用这种跨语言不一致性作为混合专家（MoE）语言模型可解释性工具。我们的知识定位框架对比了在正确回忆信息的语言集中的路由情况，从而隔离在回答知识问题中起功能作用的模型组件。我们的方法分两个阶段：（1）通过多样化的语言集查询困难事实问题，生成“成功”和“失败”激活桶；（2）对MoE路由器日志应用统计对比分析，识别关键专家。为验证少量专家对回答知识问题的必要性，我们禁用它们并重新提问。我们发现，尽管仅禁用约6000个专家中的20个，模型在超过40%的情况下不再正确回答。通常，这种方法提供了一种现实且可扩展的知识定位方法，以应对日益复杂的LLM。
英文摘要：
Modern LLMs continue to exhibit significant variance in behavior across languages, such as being able to recall factual information in some languages but not others. While typically studied as a problem to be mitigated, in this work, we propose leveraging this cross-lingual inconsistency as a tool for interpretability in mixture-of-experts (MoE) LLMs. Our knowledge localization framework contrasts routing for sets of languages where the model correctly recalls information from languages where it fails. This allows us to isolate model components that play a functional role in answering about a piece of knowledge. Our method proceeds in two stages: (1) querying the model with difficult factual questions across a diverse set of languages to generate "success" and "failure" activation buckets and then (2) applying a statistical contrastive analysis to the MoE router logits to identify experts important for knowledge. To validate the necessity of this small number of experts for answering a knowledge question, we deactivate them and re-ask the question. We find that despite only deactivating about 20 out of 6000 experts, the model no longer answers correctly in over 40% of cases. Generally, this method provides a realistic and scalable knowledge localization approach to address increasingly complex LLMs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
