Papers with Upper Confidence Bound
Principled Self-Correction in Discrete Diffusion: A UCB-Guided Framework for Text Generation (2026.eacl-long)
Copied to clipboard
| Challenge: | Existing diffusion models are trained on corrupted ground-truth tokens, but at inference time they must denoise inputs corruptes from their own predictions. |
| Approach: | They propose a framework that denoises inputs corrupted from their own predictions at inference time. |
| Outcome: | The proposed framework achieves higher faithfulness and coherence over existing diffusion baselines. |
Be Your Own Red Teamer: Safety Alignment via Self-Play and Reflective Experience Replay (2026.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have remarkable capabilities but are vulnerable to adversarial “jailbreak” attacks designed to bypass safety guardrails. |
| Approach: | They propose to empower a large language model to be its own red teamer . safety self-play allows the model to act as both the Attacker and Defender . |
| Outcome: | The proposed approach outperforms baselines trained on static adversarial datasets and establishes a new benchmark for proactive safety alignment. |
ClusterUCB: Efficient Gradient-Based Data Selection for Targeted Fine-Tuning of LLMs (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Gradient-based data influence approximation is not feasible in practice. |
| Approach: | They propose a gradient-based data selection framework with clustering and a modified Upper Confidence Bound algorithm to solve this problem. |
| Outcome: | The proposed framework can achieve comparable results to the original gradient-based data selection methods while reducing computational consumption. |