Rethinking LLM Evaluation: Vishakha Agrawal Proposes Groundbreaking Framework to Evaluate LLMs beyond Text Prediction

As language models evolve rapidly, so does the challenge of evaluating their true capabilities. Traditional benchmarks, centered on rote Q&A or string prediction, fall short of capturing how these models perform in dynamic, real-world scenarios. In this shifting landscape, the focus is no longer just on what a model can answer, but how it reasons, adapts, and behaves when deployed in complex, high-stakes environments.

Vishakha Agrawal, an AI Engineer at AMD, is addressing this gap head-on. In her recent paper, “Beyond Next Word Prediction: Developing Comprehensive Evaluation Frameworks for Measuring LLM Performance on Real World Applications,” she proposes a paradigm shift in how we assess large language models (LLMs). “These models aren’t just completing sentences,” Agrawal explains. “They’re influencing decisions in finance, law, and healthcare. Evaluating them like glorified autocomplete engines misses the bigger picture.”

Her proposed evaluation framework is designed to mirror the unpredictability of real-world tasks. Drawing from dynamic settings such as games and simulations, the framework tests how LLMs reason, act, and adapt under pressure. Models are placed in interactive environments ranging from financial markets to strategic games, where performance is measured not only by accuracy, but also by decision traceability, adaptability, usability, and ethical behavior.

What sets this approach apart is its versatility. By abstracting scenarios into a generalized state-action format, the framework allows researchers and enterprises to evaluate LLMs across diverse applications, from customer service to enterprise automation. This method enables a full-spectrum view of model behavior, surfacing not just what the model does, but why it does it.

As more organizations embed LLMs into mission-critical operations, Agrawal’s work arrives at a pivotal moment. Her framework introduces built-in red-teaming, stress-testing models in simulated conditions to uncover safety concerns like manipulative reasoning or unintended bias, before they reach real users. It's a proactive, systems-level approach to AI governance.

In a field that often chases leaderboard scores and state-of-the-art metrics, she redirects attention toward relevance and responsibility. She advocates for evaluations that simulate messy, nuanced contexts, environments where performance is not just measured by correctness, but by coherence, accountability, and resilience.

One of the most striking aspects of her work is its ability to uncover strategic and ethical risks. In simulated scenarios, models may exhibit competitive behavior that disregards rules or user intent. By embedding these edge cases into test conditions, her framework brings such risks to the forefront, thus allowing developers to design smarter, more ethical systems from the start.

Beyond theory, the paper provides a practical roadmap for implementation. From launching simulation-based pilots to conducting model audit trails, her recommendations are actionable. Enterprises can use her task suitability matrix to align LLM capabilities with domain-specific needs, whether in legal advisory, autonomous tool operation, or AI-assisted financial planning. This opens the door to smarter, safer human-AI collaboration.

As generative AI moves from novelty to necessity, frameworks like Agrawal’s offer a crucial reality check. They reveal how models behave when the stakes are real. And in doing so, they lay the foundation for evaluating AI not just as a tool, but as a responsible system.