Large Language Model
Man Ho Lam 1 Eric John Li
Evaluating Large Language Models' (LLMs) anthropomorphic capabilities has
StrategyLLM: Large Language Models as Strategy Generators, Executors, Optimizers, and Evaluators for Problem Solving Chang Gao
It employs four LLM-based agents: strategy generator, executor, optimizer, and evaluator, working together to generate, evaluate, and select promising strategies for a given task. Experimental results demonstrate that StrategyLLM outperforms the competitive baseline CoT -SC that requires human-annotated solutions on 13 datasets across 4 challenging tasks without human involvement, including math reasoning (34.2%
A Stylized Image Dataset for Enhanced Machine Unlearning Evaluation in Diffusion Models
However, existing MU evaluation systems present several key challenges that can result in incomplete and inaccurate assessments.
A distributional simplicity bias in the learning dynamics of transformers
The remarkable capability of over-parameterised neural networks to generalise effectively has been explained by invoking a "simplicity bias": neural networks prevent overfitting by initially learning simple classifiers before progressing to
