Papers by Junmin Zhu

2 papers
CNNSum: Exploring Long-Context Summarization with Large Language Models in Chinese Novels (2025.findings-acl)

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Challenge: Currently, long-context summarization mainly relies on memory ability.
Approach: They propose a multi-scale long-context summarization benchmark based on Chinese novels . they use human-driven annotations to analyze long-constituency models .
Outcome: The proposed benchmark features human-driven annotations across four subsets with lengths ranging from 16k to 128k.
LIFBench: Evaluating the Instruction Following Performance and Stability of Large Language Models in Long-Context Scenarios (2025.acl-long)

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Challenge: Existing benchmarks rarely focus on instruction-following in long-context scenarios or stability on different inputs.
Approach: They propose a scalable dataset to evaluate LLMs’ instruction-following capabilities and stability across long contexts.
Outcome: The proposed method evaluates LLMs’ instruction-following capabilities and stability across long contexts.

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