少即是多：在大型語言模型特徵空間中合成多樣化數據

摘要

大型語言模型中，訓練後數據的多樣性對於下游任務效能至關重要。現有許多構建訓練後數據的方法採用基於文本的指標來量化多樣性，這些指標雖能捕捉語言變異，但對決定下游效能的任務相關特徵僅能提供微弱信號。本研究提出「特徵激活覆蓋率」（FAC），透過可解釋的特徵空間來衡量數據多樣性。基於此指標，我們進一步設計出名為「FAC合成法」的多樣性驅動數據生成框架：先使用稀疏自編碼器識別種子數據集中缺失的特徵，再明確生成反映這些特徵的合成樣本。實驗表明，我們的方法在指令遵循、毒性檢測、獎勵建模及行為導向等多項任務中，持續提升數據多樣性與下游效能。值得注意的是，我們發現不同模型系列（如LLaMA、Mistral、Qwen）間存在共享的可解釋特徵空間，從而實現跨模型知識遷移。本研究為探索以數據為中心的大型語言模型優化提供了堅實且實用的方法論。

English

The diversity of post-training data is critical for effective downstream performance in large language models (LLMs). Many existing approaches to constructing post-training data quantify diversity using text-based metrics that capture linguistic variation, but such metrics provide only weak signals for the task-relevant features that determine downstream performance. In this work, we introduce Feature Activation Coverage (FAC) which measures data diversity in an interpretable feature space. Building upon this metric, we further propose a diversity-driven data synthesis framework, named FAC Synthesis, that first uses a sparse autoencoder to identify missing features from a seed dataset, and then generates synthetic samples that explicitly reflect these features. Experiments show that our approach consistently improves both data diversity and downstream performance on various tasks, including instruction following, toxicity detection, reward modeling, and behavior steering. Interestingly, we identify a shared, interpretable feature space across model families (i.e., LLaMA, Mistral, and Qwen), enabling cross-model knowledge transfer. Our work provides a solid and practical methodology for exploring data-centric optimization of LLMs.

少即是多：在大型語言模型特徵空間中合成多樣化數據

Less is Enough: Synthesizing Diverse Data in Feature Space of LLMs

摘要

Support