部分情感更深层：大语言模型中的分层探测与因果干预
Some Emotions Run Deeper: Layer-wise Probing and Causal Intervention in Large Language Models
- Université Sorbonne Paris Nord（巴黎北大学）
- CNRS（法国国家科学研究中心）
- LORIA（洛里亚实验室）
- Université de Lorraine（洛林大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究针对三类情感表达数据集与八款开源大模型，结合分层探测等方法发现情感探测层随语料库偏移，干预选定频段可显著降准，选定频段可跨数据集迁移，早出口表示性能优于全深度出口。
AI中文摘要：
情感在文本中以宽泛的范围呈现，从表层词汇线索到与内容交织的推理。大多数针对大语言模型（LLM）情感的分层分析使用单一语料库，未明确情感可被获取的深度是模型的属性还是文本来源的属性。我们在三个涵盖不同情感表达明确度与语境化程度的数据集（Twitter 帖子、Reddit 评论和自传体叙事）以及八个来自 Llama、Qwen 和 Granite 系列的 1B 至 9B 开放权重 LLM 上开展研究。我们将分层探测与离线特征缩放、在线前向干预、迁移分析及早出口分类器相结合。研究发现：（i）最优探测层在不同语料库间系统偏移，从输入相邻层到超过模型深度的一半，且在按长度区间匹配标签分布后该排序仍存在；（ii）在所有评估设置中，对探测选定的频段进行前向干预，使测试准确率比相同宽度的随机频段降低 5-6 个百分点（q < 0.01）；（iii）选定的频段可跨数据集和情感类别迁移，表明存在部分共享的情感信息，而非严格的逐情感子结构；（iv）探测选定的早出口表示平均比全深度出口表现好 6.9 个百分点。
英文摘要：
Emotion is expressed in text along a wide spectrum, from surface lexical cues to inferences entangled with content. Most layer-wise analyses of emotion in LLMs use a single corpus, leaving open whether the depth at which emotion becomes accessible is a property of the model or also of the text source. We investigate this across three datasets spanning different degrees of explicitness and contextualization in emotion expression (Twitter posts, Reddit comments, and autobiographical narratives) and eight 1B--9B open-weight LLMs from the Llama, Qwen, and Granite families. We combine layer-wise probing with offline feature scaling and online forward interventions, transfer analyses, and an early-exit classifier. We find that (i) the best probing layer shifts systematically across corpora, from input-adjacent layers to over half model depth, and this ordering persists after matching label-by-length-bin distributions; (ii) across the evaluated settings, forward-pass interventions on probe-selected bands reduce test accuracy by 5--6 points more than same-width random bands ($q < 0.01$); (iii) selected bands transfer across datasets and emotion categories, suggesting partially shared affective information rather than strictly per-emotion substrates; and (iv) probe-selected early-exit representations outperform full-depth exits by $6.9$ percentage points on average.
