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Bo Pan

ACL 2026×2多模态对齐与偏好可解释性强化学习与后训练
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0 一作 · 2 篇 · ACL 2026

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2026 顶会论文

2
作者位次 #5/6ACL 2026Poster· ACL Industry Track

Automatic Prompt Engineering for Scalable Prompt Inversion in Text-to-Image Ad Generation

Zixin Ding · Qi Zeng · Boying Gong · Wenlong Deng · Bo Pan · Yuxin Chen

摘要

While prompt engineering offers effective control over Text-to-Image (T2I) generation, it remains labor-intensive for large-scale production. We present PRISM-DUEL, a black-box framework that formalizes prompt optimization as Automatic Prompt Engineering (APE), motivated by advertising workflows...

可解释性多模态对齐与偏好OpenReview 项目页
作者位次 #5/6ACL 2026Poster· ACL Industry Track

Multi-Task Reinforcement Learning for Enhanced Multimodal LLM -as-a-Judge

Junjie Wu · Xuan Kan · Zihao He · Shunwen Tan · Bo Pan · Kaitai Zhang

摘要

Multimodal Large Language Models (MLLMs) have been widely adopted as MLLM-as-aJudges due to their strong alignment with human judgment across various visual tasks. However, most existing judge models are optimized for single-task scenarios and struggle to generalize to diverse contexts, which is...

对齐与偏好多模态强化学习与后训练OpenReview 项目页

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