29 citations · 45 across the 9 of their papers we have counts for
10 papers · 1 filter
OpenCompass: A Universal Evaluation Platform for Large Language Models
Maosong Cao, Kai Chen, Haodong Duan +27
In recent years, the field of artificial intelligence has undergone a paradigm shift from task-specific small-scale models to general-purpose large language models (LLMs). With the…
CompassJudger-2: Towards Generalist Judge Model via Verifiable Rewards
Taolin Zhang, Maosong Cao, Alexander Lam +2
Recently, the role of LLM-as-judge in evaluating large language models has gained prominence. However, current judge models suffer from narrow specialization and limited robustness…
Rethinking Verification for LLM Code Generation: From Generation to Testing
Zihan Ma, Taolin Zhang, Maosong Cao +5
Large language models (LLMs) have recently achieved notable success in code-generation benchmarks such as HumanEval and LiveCodeBench. However, a detailed examination reveals that…
Coding Triangle: How Does Large Language Model Understand Code?
Taolin Zhang, Zihan Ma, Maosong Cao +3
Large language models (LLMs) have achieved remarkable progress in code generation, yet their true programming competence remains underexplored. We introduce the Code Triangle frame…
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…
CompassJudger-1: All-in-one Judge Model Helps Model Evaluation and Evolution
Maosong Cao, Alexander Lam, Haodong Duan +3
Efficient and accurate evaluation is crucial for the continuous improvement of large language models (LLMs). Among various assessment methods, subjective evaluation has garnered si…