想快速了解NLP信任研究六年的演变脉络，看这篇综述就够了，它把144篇论文的规律都总结好了。
TrustNLP研讨会自2021年起与ACL系列会议联办，论文数从8篇增至41篇，六届共144篇。综述按六类信任维度分类，发现2025-2026年真实性主题占37%，公平性最稳定，可解释性呈U型回升。与ACL等约2000篇论文对比，TrustNLP主题分布接近领域平均水平。
From Interpretability to Control: Insights from Six Years of the TrustNLP Workshop
The Workshop on Trustworthy Natural Language Processing (TrustNLP), co-located with major ACL conferences since 2021, has grown from 8 proceedings papers to 41 over six editions, documenting a field-wide transition from post-hoc interpretability of static models to mechanistic understanding and proactive control of generative systems. We synthesize insights from all 144 proceedings papers, classifying them along six trust dimensions grounded in established frameworks (TrustLLM, DecodingTrust). We observe co-occurrences with capability emergence. The release of the first high-impact chat models activated all trust dimensions simultaneously, while subsequent model generations shifted focus toward truthfulness and safety alignment. Analysis from the classification study reveals that truthfulness is the fastest-growing dimension (absent in 2021-2022, comprising 37% of papers by 2025-2026), fairness remains the most consistent theme, and explainability exhibits a U-shaped trajectory; declining as post-hoc methods lost relevance but resurging in 2026 through mechanistic interpretability. A cross-venue comparison with ACL, NAACL, EACL, and EMNLP (~2K papers) in the same period shows that TrustNLP's topical distribution closely follows the field average. We identify four structural insights and conclude with actionable directions for the research community.