Workshop Summary

Workshop date: December 7th. 

As large language models (LLMs) rapidly integrate into diverse applications, the pressing challenge is not just to evaluate their current performance, but to define the next generation of evaluation protocols for increasingly capable and complex LLMs. This workshop directly addresses this critical need for robust methodologies and best practices across the entire LLM lifecycle – from foundational pre-training to advanced post-training techniques like reinforcement learning from human feedback (RLHF). 

Our workshop aims to forge a comprehensive understanding of LLM evaluation, emphasizing its interrelations, emergent capabilities, scaling challenges, and the development of cutting-edge benchmarks designed for tomorrow’s models. We seek to bring together leading researchers and practitioners to discuss not only established metrics and benchmarking protocols but also the crucial aspects of assessing complex behaviors, understanding the impact of scaling on model properties, and advancing holistic evaluation frameworks that anticipate future LLM evolution. By exploring these multifaceted dimensions, we aim to accelerate the development of truly reliable and capable LLMs, equipped to handle the complexities of the real world and shape how we evaluate them next. 

Topics of interest include:  

• Evaluation metrics for pre-trained models and foundational capabilities, including emergent abilities. 

• Assessing the impact of fine-tuning and adaptation on model performance and behavior. 

• Advanced post-training evaluation techniques, including reinforcement learning from human feedback (RLHF) and human-in-the-loop assessments.

•  Interrelations and dependencies between different evaluation stages and their impact on model generalization.

• Benchmarking, standardization of evaluation protocols, and the development of new, challenging evaluation paradigms (e.g., using LLMs as judges).

•  Understanding and evaluating scaling laws in relation to model performance and emergent phenomena. 

• Addressing data contamination, memorization, and other data-centric evaluation challenges. 

• Developing and applying holistic evaluation frameworks for diverse LLM capabilities.

•  Evaluating the evolution of LLM capabilities and potential risks as models scale.