开发者应该关心可解释性吗？

Should Developers Care about Interpretability?

塔里克·希希帕尔 Thariq Shihipar · Anthropic · 2024-11-04 · Blog ↗

摘要 · Abstract

可解释性和导引是如何工作的？承诺有哪些应用？1. 捕捉无法用言语描述的风格 2. 减少 RLHF 需求 3. 记住用户偏好与用户请求 4. 廉价、快速、可复现的分类缺点有哪些？1. 使模型‘偏离分布’ 2. 不理解特征的作用 3. 激活其他特征和电路结论 Thariq Shihipar - 2024 年 11 月 4 日 · 6 分钟阅读今年 LLM 研究中最大的突破之一就是可解释性——即理解 LLM 在“思考”什么的能力。

How does Interpretability and Steering work?The PromiseWhat are the applications?1. Capturing style that cannot be described in words2. Less need for RLHF3. Remembering User Preferences vs User Requests4. Cheap, Fast, Reproducible ClassificationWhat are the downsides?1. Moving the model ‘out of distribution’2. Not understanding what a feature does3. Activating other features & circuitsConclusion Thariq Shihipar - 4 November 2024 · 6 min read Arguably the biggest breakthrough in LLM research this year has been in interpretability- the ability to understand what a LLM is “thinking”.

核心贡献 · Key contributions

可解释性通过特征使理解 LLM 内部表征成为可能。
Interpretability enables understanding LLM internal representations via features.
特征引导允许在推理时对模型输出进行细粒度控制。
Feature steering allows fine-grained control of model outputs at inference.
引导通过即时激活/停用特征减少对基于人类反馈的强化学习（RLHF）的需求。
Steering reduces need for RLHF by activating/deactivating features on-the-fly.
应用包括捕捉难以言说的风格、廉价分类以及持久化用户偏好。
Applications include capturing indefinable style, cheap classification, and persistent user preferences.
引导适用于多种模态：文本、语音和图像。
Steering works across modalities: text, voice, and images.
为 API 开发者提供比统一基于人类反馈的强化学习（RLHF）策略更具体的控制。
Provides API developers with more specific control than blanket RLHF policies.

局限 · Limitations

引导可能使模型偏离分布，导致生成不连贯文本。
Steering can push the model out of distribution, producing incoherent text.
特征标签不完善；错误标签会降低引导的可靠性。
Feature labels are imperfect; mislabeling can reduce steering reliability.
激活一个特征可能无意中触发其他特征或电路，导致副作用。
Activating a feature may inadvertently trigger other features or circuits, causing side effects.
特征引导尚未大规模应用，因此其可靠性不确定。
Wide-scale use of feature steering remains unproven, making reliability uncertain.
引导的复杂性可能要求开发者超越提示和检索增强生成（RAG）方法。
The complexity of steering may require developers to move beyond prompting and RAG.

论文章节 · Sections（共 14）

内容 CONTENTS
可解释性与引导如何工作？ How does Interpretability and Steering work?
前景 The Promise
1. 捕捉难以言表的风格 1. Capturing style that cannot be described in words
文本风格 Text Styles
语音风格 Voice Styles
图像风格 Image Styles
2. 对 RLHF 的需求减少 2. Less need for RLHF
3. 记住用户偏好与用户请求 3. Remembering User Preferences vs User Requests
4. 廉价、快速、可复现的分类 4. Cheap, Fast, Reproducible Classification
1. 将模型移出分布 1. Moving the model ‘out of distribution’
2. 不理解特征的作用 2. Not understanding what a feature does
3. 激活其他特征与回路 3. Activating other features & circuits
结论 Conclusion

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