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Success is in the Details: Evaluate and Enhance Details Sensitivity of Code LLMs through Counterfactuals
Xianzhen Luo, Qingfu Zhu, Zhiming Zhang +8
Code Sensitivity refers to the ability of Code LLMs to recognize and respond to details changes in problem descriptions. While current code benchmarks and instruction data focus on…
Turning Trash into Treasure: Accelerating Inference of Large Language Models with Token Recycling
Xianzhen Luo, Yixuan Wang, Qingfu Zhu +4
Massive parameters of LLMs have made inference latency a fundamental bottleneck. Speculative decoding represents a lossless approach to accelerate inference through a guess-and-ver…
Semi-Instruct: Bridging Natural-Instruct and Self-Instruct for Code Large Language Models
Xianzhen Luo, Qingfu Zhu, Zhiming Zhang +4
Instruction tuning plays a pivotal role in Code Large Language Models (Code LLMs) for the task of program synthesis. Presently, two dominant paradigms for collecting tuning data ar…
Python is Not Always the Best Choice: Embracing Multilingual Program of Thoughts
Xianzhen Luo, Qingfu Zhu, Zhiming Zhang +5
Program of Thoughts (PoT) is an approach characterized by its executable intermediate steps, which ensure the accuracy of the logical calculations in the reasoning process. Current…