📖 NLP Understanding

🧪 ICML2026 · 2 paper notes

📌 Same area in other venues: 🔬 ICLR2026 (2) · 💬 ACL2026 (34) · 🤖 AAAI2026 (1) · 🧠 NeurIPS2025 (3) · 📹 ICCV2025 (1) · 🧪 ICML2025 (1)

Causal Fine-Tuning under Latent Confounded Shift

This paper proposes Causal Fine-Tuning (CFT): an SCM-inspired decomposition of "high-level stable features \(C\) + low-level confounding-sensitive features \(\Phi\)" is embedded into standard BERT fine-tuning. By utilizing a front-door style do-calculus adjustment for prediction, it significantly outperforms single-domain generalization baselines such as SFT/SWA/WISE under text spurious correlation injection attacks.

Controlling the Risk of Corrupted Contexts for Language Models via Early-Exiting

This paper formalizes the problem where "user-provided corrupted contexts degrade LLM performance" as a risk control task. By using zero-shot performance as a "safety baseline," combining dynamic early-exit (predicting at intermediate layers to avoid late-layer overthinking of harmful contexts) with a context-aware loss and an improved Learn-then-Test framework (preserving negative loss values via risk transformation rather than clipping), this method guarantees risk \(\leq\) user-specified \(\epsilon\) while achieving \(> 50\%\) computational acceleration across 9 tasks.