5 papers
Echo: Learning from Experience Data via User-Driven Refinement
Hande Dong, Xiaoyun Liang, Jiarui Yu +15
Static "human data" faces inherent limitations: it is expensive to scale and bounded by the knowledge of its creators. Continuous learning from "experience data" - interactions bet…
From Token to Line: Enhancing Code Generation with a Long-Term Perspective
Tingwei Lu, Yangning Li, Liyuan Wang +6
The emergence of large language models (LLMs) has significantly promoted the development of code generation task, sparking a surge in pertinent literature. Current research is hind…
Consensus dimension reduction via multi-view learning
Bingxue An, Tiffany M. Tang
A plethora of dimension reduction methods have been developed to visualize high-dimensional data in low dimensions. However, different dimension reduction methods often output diff…
Code Copycat Conundrum: Demystifying Repetition in LLM-based Code Generation
Mingwei Liu, Juntao Li, Ying Wang +9
Despite recent advances in Large Language Models (LLMs) for code generation, the quality of LLM-generated code still faces significant challenges. One significant issue is code rep…
Revisiting Classification Taxonomy for Grammatical Errors
Deqing Zou, Jingheng Ye, Yulu Liu +7
Grammatical error classification plays a crucial role in language learning systems, but existing classification taxonomies often lack rigorous validation, leading to inconsistencie…