Search Results for author:
Found 3 papers, 1 papers with code
DyVal 2: Dynamic Evaluation of Large Language Models by Meta Probing Agents
Our multifaceted analysis demonstrated the strong correlation between the basic abilities and an implicit Matthew effect on model size, i. e., larger models possess stronger correlations of the abilities.
PromptBench: A Unified Library for Evaluation of Large Language Models
The evaluation of large language models (LLMs) is crucial to assess their performance and mitigate potential security risks.
CompeteAI: Understanding the Competition Behaviors in Large Language Model-based Agents
Large language models (LLMs) have been widely used as agents to complete different tasks, such as personal assistance or event planning.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
