Papers by Chenjia Bai
Towards Efficient LLM Grounding for Embodied Multi-Agent Collaboration (2025.findings-acl)
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| Challenge: | Existing methods for grounding large language models suffer from inefficient querying . Existing approaches that rely on physical verification or self-reflection suffer from excessive querying. |
| Approach: | They propose a framework that introduces Reinforced Advantage feedback for efficient self-refinement of plans. |
| Outcome: | The proposed framework surpasses baselines in success rate and significantly decreases interaction steps of agents and query rounds of LLMs. |
VLP: Vision-Language Preference Learning for Embodied Manipulation (2025.emnlp-main)
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| Challenge: | Existing approaches to reward engineering are time-consuming and expensive to collect human preference labels. |
| Approach: | They propose a vision-language preference learning framework which learns from human feedback . they define three types of language-conditioned preferences and construct a visual preference dataset . |
| Outcome: | The proposed framework outperforms baselines on embodied manipulation tasks and can be applied to other tasks. |
Online Iterative Self-Alignment for Radiology Report Generation (2025.acl-long)
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| Challenge: | Existing methods for RRG rely on supervised fine-tuning based on data pairs of radiological images and corresponding radiologist-annotated reports. |
| Approach: | They propose a method that performs supervised fine-tuning on data pairs of radiological images and corresponding radiologist-annotated reports. |
| Outcome: | The proposed method surpasses existing methods and achieves state-of-the-art performance across multiple evaluation metrics. |