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Where You Inject Diversity Matters: A Unified Framework for Diverse Generation
Cheng Zhang, Rui Xin, Chudi Zhong
Open-ended generation tasks often require a set of meaningfully different outputs, yet large language models often produce similar generations. Existing test-time diversity methods…
A Decomposition Perspective to Long-context Reasoning for LLMs
Yanling Xiao, Huaibing Xie, Guoliang Zhao +8
Long-context reasoning is essential for complex real-world applications, yet remains a significant challenge for Large Language Models (LLMs). Despite the rapid evolution in long-c…
Reading, Not Thinking: Understanding and Bridging the Modality Gap When Text Becomes Pixels in Multimodal LLMs
Kaiser Sun, Xiaochuang Yuan, Hongjun Liu +4
Multimodal large language models (MLLMs) can process text presented as images, yet they often perform worse than when the same content is provided as textual tokens. We systematica…
CL-bench: A Benchmark for Context Learning
Shihan Dou, Ming Zhang, Zhangyue Yin +24
Current language models (LMs) excel at reasoning over prompts using pre-trained knowledge. However, real-world tasks are far more complex and context-dependent: models must learn f…