Papers with SelfAware
Do Large Language Models Know What They Don’t Know? (2023.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) have vast knowledge that allows them to excel in various NLP tasks. |
| Approach: | They propose an automated method to detect uncertainty in the responses of large language models and a dataset to measure their self-knowledge. |
| Outcome: | The proposed method detects uncertainty in the responses of large language models and provides a novel measure of their self-knowledge. |
Abstain-R1: Calibrated Abstention and Post-Refusal Clarification via Verifiable RL (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing abstention methods produce generic refusals or encourage follow-up clarifications without verifying whether they identify the key missing information. |
| Approach: | They propose a clarification-aware RLVR reward that rewards correct answers on unanswerable queries while optimizing explicit abstention and semantically aligned post-refusal clarification on unannounced queries. |
| Outcome: | The proposed model improves abstention and clarification on unanswerable queries while maintaining strong performance on answerable queries. |