Papers by Wenbo Hu
CE-RM: A Pointwise Generative Reward Model Optimized via Two-Stage Rollout and Unified Criteria (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing studies have shown that rule-based evaluation methods are ineffective for open-ended natural language generation. |
| Approach: | They propose a pointwise generative reward model with a dedicated two-stage rollout method and unified query-based criteria that can be trained with 5.7K high-quality data. |
| Outcome: | The proposed model achieves superior performance on diverse reward model benchmarks, especially in Best-of-N scenarios, and delivers more effective improvements in downstream RL practice. |
VALOR-EVAL: Holistic Coverage and Faithfulness Evaluation of Large Vision-Language Models (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing evaluation methods focus on object hallucinations, focusing on object outputs . current evaluation methods struggle to address subtle semantic distinctions between outputs and reference data . |
| Approach: | They propose a multi-dimensional benchmark covering objects, attributes, and relations . they propose metric that generalizes CHAIR metric and incorporates faithfulness and coverage . |
| Outcome: | The proposed evaluation framework is more comprehensive and better correlated with humans than existing evaluation methods. |
SURE: Safety Understanding and Reasoning Enhancement for Multimodal Large Language Models (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing multimodal large language models incorporate visual and textual information, but introduces new and complex safety risks. |
| Approach: | They propose a safety reasoning framework that integrates visual modalities into multimodal models to help them resist jailbreak attacks. |
| Outcome: | The proposed framework improves model safety while avoiding over-defense . it is based on a large-scale safety reasoning dataset . |
AIR: Complex Instruction Generation via Automatic Iterative Refinement (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for generating complex instructions are resource-intensive and lack diversity. |
| Approach: | They propose a framework to generate complex instructions with constraints using a document-generated initial instruction and an iterative refinement framework to incorporate LLM-as-judge guidance. |
| Outcome: | The proposed framework significantly outperforms existing methods for generating complex instructions, and outperformed existing methods. |
SATA: A Paradigm for LLM Jailbreak via Simple Assistive Task Linkage (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to design sophisticated instructions for the LLM to follow, or rely on multiple iterations, could hinder the performance and efficiency of jailbreaks. |
| Approach: | They propose a simple assistive task linkage paradigm which masks harmful keywords within malicious queries and uses a masked language model task to encode the semantics of the mangled keywords. |
| Outcome: | The proposed paradigm can effectively circumvent LLM safeguards and elicit harmful responses. |
Chinese SimpleQA: A Chinese Factuality Evaluation for Large Language Models (2025.acl-long)
Copied to clipboard
Yancheng He, Shilong Li, Jiaheng Liu, Yingshui Tan, Weixun Wang, Hui Huang, Xingyuan Bu, Hangyu Guo, Chengwei Hu, Boren Zheng, Zhuoran Lin, Dekai Sun, Zhicheng Zheng, Wenbo Su, Bo Zheng
| Challenge: | Current frontier models sometimes generate false outputs or answers that are not substantiated by evidence. |
| Approach: | They propose Chinese SimpleQA, a Chinese benchmark to evaluate LLMs' factuality . they focus on Chinese language over 6 major topics with 99 diverse subtopics . |
| Outcome: | The Chinese SimpleQA benchmark evaluates the factuality ability of LLMs . the questions and answers are short and easy-to-evaluate . |
Unveiling Uncertainty: A Deep Dive into Calibration and Performance of Multimodal Large Language Models (2025.coling-main)
Copied to clipboard
| Challenge: | Multimodal large language models combine visual and textual data for tasks like image captioning and visual question answering. |
| Approach: | They propose temperature scaling and iterative prompt optimization to calibrate MLLMs and enhance model reliability. |
| Outcome: | The proposed techniques improve MLLMs and improve model reliability. |