4 citations · 4 across the 5 of their papers we have counts for
5 papers
Skill Training with Corruption and Reconstruction Loop
Mo Li, Zixin Yin, Ting Cao +3
Large Language Models (LLMs) often struggle in highly specialized domains. Rather than parameter-level adaptation of LLMs which is costly and difficult to interpret, external skill…
Learning to Commit: Generating Organic Pull Requests via Online Repository Memory
Mo Li, L. H. Xu, Qitai Tan +2
Large language model (LLM)-based coding agents achieve impressive results on controlled benchmarks yet routinely produce pull requests that real maintainers reject. The root cause…
Sculptor: Empowering LLMs with Cognitive Agency via Active Context Management
Mo Li, L. H. Xu, Qitai Tan +3
Large Language Models (LLMs) suffer from significant performance degradation when processing long contexts due to proactive interference, where irrelevant information in earlier pa…
Condor: Enhance LLM Alignment with Knowledge-Driven Data Synthesis and Refinement
Maosong Cao, Taolin Zhang, Mo Li +5
The quality of Supervised Fine-Tuning (SFT) data plays a critical role in enhancing the conversational capabilities of Large Language Models (LLMs). However, as LLMs become more ad…
NeedleBench: Evaluating LLM Retrieval and Reasoning Across Varying Information Densities
Mo Li, Songyang Zhang, Taolin Zhang +3
The capability of large language models to handle long-context information is crucial across various real-world applications. Existing evaluation methods often rely either on real-…