Papers with confidence elicitation
Exploiting Prompt-induced Confidence for Black-Box Attacks on LLMs (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models (LLMs) are vulnerable to adversarial attacks even in strict black-box settings with only hard-label feedback. |
| Approach: | They propose a black-box framework that leverages prompt-induced confidence as an auxiliary signal to guide attacks. |
| Outcome: | The proposed framework improves the attack success rate and query efficiency while maintaining semantic fidelity. |
GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models (2026.acl-long)
Copied to clipboard
| Challenge: | Existing methods for assessing the reliability of Large Language Models (LLMs) by confidence elicitation require expensive computational overhead or suffer from poor calibration, making them unreliable for real-world deployment. |
| Approach: | They propose a Generative Approach to Confidence Elicitation that enables reliable confidence elicitation for Large Language Models. |
| Outcome: | The proposed method achieves the best discriminative capacity and calibration on open-ended tasks without resorting to additional sampling or an auxiliary model. |
Large Language Models Are Overconfident in Their Own Responses (2026.findings-acl)
Copied to clipboard
| Challenge: | Prior work has shown that instruction-tuned large language models are less well calibrated than their base pre-trained counterparts. |
| Approach: | They propose a simple inference-time strategy that frams the model’s answer as user input during confidence elicitation. |
| Outcome: | The proposed approach reduces overconfidence and improves calibration by up to 26% without retraining. |