Papers by Peiqi Yu
Prompt-Guided Internal States for Hallucination Detection of Large Language Models (2025.acl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) generate incorrect or logically incorrect responses, which is known as LLM hallucinations. |
| Approach: | They propose a framework for supervised hallucination detection using in-domain data by prompting changes to the structure related to text truthfulness in LLMs’ internal states. |
| Outcome: | The proposed framework enhances the cross-domain generalization of existing hallucination detection methods. |
Inference Compute-Optimal Video Vision Language Models (2025.acl-long)
Copied to clipboard
| Challenge: | Using video vision language models, inference costs are often more expensive than finetuning. |
| Approach: | They investigate the optimal allocation of inference compute across three key scaling factors in video vision language models. |
| Outcome: | The proposed model configurations are based on three key scaling factors . the results can be applied to real-world tasks and tasks with fixed inference budgets. |