2 citations · 2 across the 2 of their papers we have counts for
3 papers
cs.SE2025
Optimizing Token Consumption in LLMs: A Nano Surge Approach for Code Reasoning Efficiency
Junwei Hu, Weicheng Zheng, Yihan Liu +1
With the increasing adoption of large language models (LLMs) in software engineering, the Chain of Thought (CoT) reasoning paradigm has become an essential approach for automated c…
cs.LG2024★ 2 cited
Combining Induction and Transduction for Abstract Reasoning
Wen-Ding Li, Keya Hu, Carter Larsen +11
When learning an input-output mapping from very few examples, is it better to first infer a latent function that explains the examples, or is it better to directly predict new test…
cs.SE2024
Code Repair with LLMs gives an Exploration-Exploitation Tradeoff
Hao Tang, Keya Hu, Jin Peng Zhou +4
Iteratively improving and repairing source code with large language models (LLMs), known as refinement, has emerged as a popular way of generating programs that would be too comple…