Beyond Next Word Prediction: Developing Comprehensive Evaluation Frameworks for measuring LLM performance on real world applications
This addresses the need for better evaluation methods for LLMs in enterprise and practical settings, though it appears incremental as it builds upon existing evaluation concepts.
The paper tackles the problem of evaluating Large Language Models (LLMs) beyond basic next-token prediction by proposing a comprehensive evaluation framework based on a game and tool-based architecture, which can be extended to various real-world applications like supply chain management or ethics.
While Large Language Models (LLMs) are fundamentally next-token prediction systems, their practical applications extend far beyond this basic function. From natural language processing and text generation to conversational assistants and software use, LLMs have numerous use-cases, and have already acquired a significant degree of enterprise adoption. To evaluate such models, static evaluation datasets, consisting of a set of prompts and their corresponding ground truths, are often used to benchmark the efficacy of the model for a particular task. In this paper, we provide the basis for a more comprehensive evaluation framework, based upon a traditional game and tool-based architecture that enables a more overarching measurement of a model's capabilities. For simplicity, we provide a generalized foundation that can be extended, without significant alteration, to numerous scenarios, from specific use cases such as supply chain management or financial reasoning, to abstract measurements such as ethics or safety.