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Ting Hua

ACL 2026×3ICML 2026×2KDD 2026评测与基准效率与系统多模态推理对齐与偏好
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0 一作 · 6 篇 · ACL 2026 / ICML 2026 / KDD 2026

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2026 顶会论文

6
作者位次 #3/4ACL 2026Poster· Natural Language Processing

C rochet B ench: Can Vision-Language Models Move from Describing to Doing in Crochet Domain?

Peiyu Li · Xiaobao Huang · Ting Hua · Nitesh V. Chawla

摘要

While multimodal large language models can describe visual content, their ability to generate executable procedures remains underexplored. CrochetBench presented in this paper evaluates this shift from describing to doing through fine-grained procedural reasoning in crochet: models must recognize...

多模态评测与基准推理OpenReview 项目页
作者位次 #7/10ACL 2026Findings· Findings of ACL

H yper E dit: Unlocking Instruction-based Text Editing in LLM s via Hypernetworks

Yiming Zeng · Jinghan Cao · Zexin Li · Wanhao Yu · Zhankai Ye · Dawei Xiang · Ting Hua · Xin Liu · Shangqian Gao · Tingting Yu

摘要

Large Language Models (LLMs) have fundamentally transformed natural language processing (NLP), demonstrating remarkable capabilities across a wide spectrum of tasks. However, when applied to instruction-based text editing, LLMs continue to exhibit some limitations. Different from free-form genera...

对齐与偏好OpenReview 项目页
作者位次 #3/5ACL 2026Findings· Findings of ACL

Context Attribution with Multi-Armed Bandit Optimization

DENG PAN · Keerthiram Murugesan · Ting Hua · Nuno Moniz · Nitesh V. Chawla

摘要

Understanding which parts of the retrieved context contribute to a large language model’s generated answer is essential for building interpretable and trustworthy retrieval-augmented generation. We propose a novel framework that formulates context attribution as a combinatorial multi-armed bandit...

评测与基准可解释性效率与系统OpenReview 项目页

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