发现大模型多语言校准差，提出用中间层提升非英语准确性。
Beyond the Final Layer: Intermediate Representations for Better Multilingual Calibration in Large Language Models
- 分析多语言模型中间层，发现末层信号不可靠
- 晚中期层在100+语言中表现更稳定、校准更好
- 无需训练，自适应选层提升非英语模型可信度
置信度校准是确保大模型部署可靠性的关键，但在多语言场景下仍缺乏研究。本文首次系统评估六类模型、超过100种语言的多语言校准性能，发现非英语语言普遍校准较差。通过分析内部表示，我们发现末层受英语主导训练影响，无法有效反映多语言置信度；而晚中期层则始终提供更可靠、更校准良好的信号。基于此，提出无需训练的方法LACE（Language-Aware Confidence Ensemble），可为每种语言自适应选择最优层组合。研究揭示了英语中心对齐的隐性成本，为构建更具全球公平性和可信度的大模型提供了新路径。
原文摘要 · Abstract (English)
Confidence calibration, the alignment of a model's predicted confidence with its actual accuracy, is crucial for the reliable deployment of Large Language Models (LLMs). However, this critical property remains largely under-explored in multilingual contexts. In this work, we conduct the first large-scale, systematic studies of multilingual calibration across six model families and over 100 languages, revealing that non-English languages suffer from systematically worse calibration. To diagnose this, we investigate the model's internal representations and find that the final layer, biased by English-centric training, provides a poor signal for multilingual confidence. In contrast, our layer-wise analysis uncovers a key insight that late-intermediate layers consistently offer a more reliable and better-calibrated signal. Building on this, we introduce a suite of training-free methods, including Language-Aware Confidence Ensemble (LACE), which adaptively selects an optimal ensemble of layers for each specific language. Our study highlights the hidden costs of English-centric alignment and offer a new path toward building more globally equitable and trustworthy LLMs by looking beyond the final layer.
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