Catwalk: A Unified Language Model Evaluation Framework for Many Datasets (2312.10253v1)

Abstract: The success of LLMs has shifted the evaluation paradigms in NLP. The community's interest has drifted towards comparing NLP models across many tasks, domains, and datasets, often at an extreme scale. This imposes new engineering challenges: efforts in constructing datasets and models have been fragmented, and their formats and interfaces are incompatible. As a result, it often takes extensive (re)implementation efforts to make fair and controlled comparisons at scale. Catwalk aims to address these issues. Catwalk provides a unified interface to a broad range of existing NLP datasets and models, ranging from both canonical supervised training and fine-tuning, to more modern paradigms like in-context learning. Its carefully-designed abstractions allow for easy extensions to many others. Catwalk substantially lowers the barriers to conducting controlled experiments at scale. For example, we finetuned and evaluated over 64 models on over 86 datasets with a single command, without writing any code. Maintained by the AllenNLP team at the Allen Institute for Artificial Intelligence (AI2), Catwalk is an ongoing open-source effort: https://github.com/allenai/catwalk.

• The paper presents a unified framework that reduces evaluation complexity from nm to n+m implementations for scalable LLM benchmarking.
• It introduces model wrappers and standardized interfaces that support encoder-only, decoder-only, and encoder/decoder models for flexible assessments.
• Extensive experiments reveal performance nuances between finetuning and zero-shot/few-shot methods, fostering reproducible and comparative research.

An Examination of Catwalk: A Unified LLM Evaluation Framework

The paper "Catwalk: A Unified LLM Evaluation Framework" presents a comprehensive approach to address challenges in evaluating LLMs across various NLP datasets and tasks. Catwalk proposes a unified framework that simplifies the comparison of LLMs by standardizing the interaction between different models and datasets.

The Problem with Fragmented Evaluations

The advent and proliferation of LLMs have created a new landscape in NLP, where models are not just task-specific but can generalize across multiple tasks. This shift presents several engineering challenges due to fragmented efforts leading to countless incompatible codebases and data formats. Typically, evaluating n models across m datasets demands nm custom implementations. Catwalk aims to address this issue by reducing the complexity to n + m implementations, thereby enabling easier scalability and controlled comparisons.

Features and Contributions of Catwalk

Catwalk introduces several key features that facilitate the benchmarking of numerous models against diverse datasets:

• Unified Interfaces and Abstractions: By providing a common interface, Catwalk facilitates the adoption of a variety of model types, including encoder-only, decoder-only, and encoder/decoder models. This abstraction not only supports finetuning but also incorporates zero-shot and few-shot evaluation strategies.
• Model Wrappers: The introduction of model wrappers in Catwalk encapsulates different evaluation strategies, enabling systematic comparisons. These wrappers accommodate prompt-based evaluations using both human-readable and machine-readable formats.
• Caching and Reusability: To optimize computational efficiency, Catwalk employs caching features that store intermediate artifacts like loaded models and dataset states. This leads to a decrease in redundant computational tasks and an enhanced utilization of hardware resources.

Numerical Results and Case Studies

The paper provides extensive experimental results showcasing Catwalk's capabilities through a matrix of models and datasets. Numerical analysis demonstrates how finetuning generally results in superior performance over zero-shot methods. However, it also highlights scenarios where zero-shot or few-shot techniques remain competitive, emphasizing the flexibility Catwalk offers in terms of evaluation characteristics.

Additionally, the paper reveals variations in dataset difficulty, with models performing consistently well on certain datasets like SciQ and experiencing challenges with datasets like LogiQA. Observations suggest significant correlations between model performance rankings across certain types of datasets, instigating further exploration into dataset characteristics and model adaptability.

Implications and Future Directions

Catwalk's unified evaluation infrastructure has profound implications for future NLP research. Notably, it provides standardized metrics and a shared benchmarking resource that fosters reproducibility and comparability across studies. This aligns with the growing need for transparency and replicability in AI research.

Moving forward, there are several intriguing avenues for exploration inspired by Catwalk. These include examining the predictive power of perplexity metrics on downstream tasks, investigating the impact of prompt design on evaluation outcomes, and exploring the cognitive emergence during model training phases. Furthermore, Catwalk's adaptability invites contributions from the NLP community to further extend its dataset and model list, nurturing a collaborative and open-source research environment.

In summary, Catwalk positions itself as a pivotal tool in the ecosystem of LLM evaluation, providing a robust, scalable, and versatile framework that addresses longstanding challenges in NLP model assessment. It empowers researchers to conduct comprehensive evaluations more efficiently, paving the way for more nuanced insights into model performance and capabilities across diverse linguistic challenges.