Beyond Next Word Prediction: Developing Comprehensive Evaluation Frameworks for measuring LLM performance on real world applications
Vishakha Agrawal, Archie Chaudhury, Shreya Agrawal
TL;DR
This paper addresses the gap between next-word prediction and real-world LLM deployment by proposing a comprehensive evaluation framework that combines game-based testing, environment-driven benchmarks, and practical use-case simulations. It introduces a state-transition, tool-enabled evaluation framework with scaffolding, allowing LLMs to interact with external environments over discrete turns and to reveal reasoning traces. Key contributions include a formal evaluation setup, concrete exemplars (Pokemon Red, trading-simulation), and a task-suitability matrix to match capabilities with use cases, plus practical implementation guidance. The approach aims to improve realism, safety analysis, and deployment decisions for LLMs in domains such as finance, law, and ethics.
Abstract
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.