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cs.LG2025
DynamicBench: Evaluating Real-Time Report Generation in Large Language Models
Jingyao Li, Hao Sun, Zile Qiao +5
Traditional benchmarks for large language models (LLMs) typically rely on static evaluations through storytelling or opinion expression, which fail to capture the dynamic requireme…
cs.LG2023
BAL: Balancing Diversity and Novelty for Active Learning
Jingyao Li, Pengguang Chen, Shaozuo Yu +2
The objective of Active Learning is to strategically label a subset of the dataset to maximize performance within a predetermined labeling budget. In this study, we harness feature…
cs.LG2023
MoTCoder: Elevating Large Language Models with Modular of Thought for Challenging Programming Tasks
Jingyao Li, Pengguang Chen, Bin Xia +2
Large Language Models (LLMs) have showcased impressive capabilities in handling straightforward programming tasks. However, their performance tends to falter when confronted with m…