ConSens: Assessing context grounding in open-book question answering
This addresses the challenge of ensuring LLM responses are context-grounded rather than relying on potentially flawed parametric knowledge, offering a scalable and interpretable evaluation method for open-book QA systems.
The paper tackled the problem of evaluating whether large language models (LLMs) ground their answers in provided context in open-book question answering, by proposing a novel metric based on perplexity contrast that effectively identifies context reliance, with experiments demonstrating its validity.
Large Language Models (LLMs) have demonstrated considerable success in open-book question answering (QA), where the task requires generating answers grounded in a provided external context. A critical challenge in open-book QA is to ensure that model responses are based on the provided context rather than its parametric knowledge, which can be outdated, incomplete, or incorrect. Existing evaluation methods, primarily based on the LLM-as-a-judge approach, face significant limitations, including biases, scalability issues, and dependence on costly external systems. To address these challenges, we propose a novel metric that contrasts the perplexity of the model response under two conditions: when the context is provided and when it is not. The resulting score quantifies the extent to which the model's answer relies on the provided context. The validity of this metric is demonstrated through a series of experiments that show its effectiveness in identifying whether a given answer is grounded in the provided context. Unlike existing approaches, this metric is computationally efficient, interpretable, and adaptable to various use cases, offering a scalable and practical solution to assess context utilization in open-book QA systems.