Papers by Haitao Leng

4 papers
GODBench: A Benchmark for Multimodal Large Language Models in Video Comment Art (2025.acl-long)

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Challenge: Existing benchmarks for video comment art are constrained by their limited modalities and insufficient categories, hindering creativity in video-based comment art creation.
Approach: They propose a benchmark that integrates video and text modalities to evaluate MLLMs’ abilities to compose video Comment art.
Outcome: The proposed framework integrates video and text modalities to evaluate MLLMs’ abilities to compose video comment art.
KwaiChat: A Large-Scale Video-Driven Multilingual Mixed-Type Dialogue Corpus (2025.findings-naacl)

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Challenge: Currently, video-based dialogue systems rely on a single dialogue type, hindering their versatility in practical applications.
Approach: They propose to generate video-driven multilingual mixed-type dialogues using KwaiChat . they propose to create a video-based multilingual mix of 4 dialogue types, 30 domains, 4 languages, 13 topics .
Outcome: The proposed model performs best on KwaiChat but is not perfect in this situation.
Live-Aid: A Large-Scale Dialogue Dataset and Benchmark for Interleaved Multi-party Interactions in Live Streaming (2026.findings-acl)

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Challenge: Existing Multimodal Large Language Models struggle with dynamic interactions due to the scarcity of high-quality interleaved data.
Approach: They propose a large-scale interleaved live interaction Chinese dataset with human-annotated video responses.
Outcome: The proposed model can be used to evaluate live interactions in Chinese over 1,100 hours and 80,037 dialogue turns.
MidMed: Towards Mixed-Type Dialogues for Medical Consultation (2023.acl-long)

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Challenge: Current medical dialogue systems assume that patients have explicit goals but are often unavailable in real-world situations due to the lack of medical knowledge.
Approach: They propose a human-to-human mixed-type medical consultation dialogue corpus . they build benchmarking baselines on MidMed and propose an instruction-guiding framework . Experimental results show the effectiveness of InsMed .
Outcome: The proposed system can help patients clarify their goals in real-world situations . it covers four departments with 8,309 dialogues and provides benchmarking baselines .

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