Papers by Qidong Su

2 papers
MMRC: A Large-Scale Benchmark for Understanding Multimodal Large Language Model in Real-World Conversation (2025.acl-long)

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Challenge: Existing multimodal large language models lack the ability to memorize, recall, and reason in sustained interactions.
Approach: They propose a multimodal real-world conversation benchmark for evaluating open-ended abilities of multimodal large language models.
Outcome: The proposed benchmarks show that the models perform better in open-ended conversations.
APPL: A Prompt Programming Language for Harmonious Integration of Programs and Large Language Model Prompts (2025.acl-long)

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Challenge: Large Language Models (LLMs) are capable of handling diverse tasks with well-crafted prompts and integration of external tools.
Approach: They propose a prompt programming language that acts as a bridge between computer programs and LLMs by providing convenient conventions between them.
Outcome: The proposed language is intuitive, concise, and efficient through representative scenarios including Chain-of-Thought with self-consistency (CoT-SC) and ReAct tool-use agent.

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