Large Language Model
Transformers are Minimax Optimal Nonparametric In-Context Learners Juno Kim 1,2 T ai Nakamaki 1 T aiji Suzuki 1,2 1 University of Tokyo 2
Large language models (LLMs) have demonstrated remarkable capabilities in understanding and generating natural language data.
BABILong: Testing the Limits of LLMs with Long Context Reasoning-in-a-Haystack
To bridge this gap, we introduce the BABILong benchmark, designed to test language models' ability to reason across facts distributed in extremely
Overleaf Example
Recent studies have shown promising results on utilizing large pre-trained image-language models for video question answering.
RedCode: RiskyCodeExecutionandGeneration BenchmarkforCodeAgents
Unsafe operations described in natural text lead to a lower rejection rate than thoseincodeformat.
Elo Uncovered: Robustness and Best Practices in Language Model Evaluation
However, while popular, the system's suitability for assessing entities with constant skill levels, such as LLMs, remains relatively unexplored. We study two fundamental axioms that evaluation methods should adhere to: reliability and transitivity .
