Papers by Hossein Hosseini Kasnavieh
IntroLM: Introspective Language Models via Prefilling-Time Self-Evaluation (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to predict output quality of large language models rely on external classifiers with limited context windows and constrained representational capacity. |
| Approach: | They propose a method that enables causal language models to predict their own output quality during the prefilling phase without affecting generation using [CPX] tokens. |
| Outcome: | The proposed method outperforms existing classifiers on Qwen3-8B and DeBERTa-v3-Large models by 14% on question-answering benchmarks. |