Seungyoon Lee, Minhyuk Kim, Seongtae Hong, Youngjoon Jang, Dongsuk Oh, Heuiseok Lim. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

As the utilization of Large Language Models (LLMs) becomes more widespread, there is a growing demand for their ability to handle more complex and longer external knowledge across various use cases.Most existing evaluations of the open-ended question answering (ODQA) task, which necessitates the use of external knowledge, focus solely on whether the model provides the correct answer.However, even when LLMs answer correctly, they often fail to provide an obvious source for their responses.Therefore, it is necessary to jointly evaluate and verify the correctness of the answers and the appropriateness of grounded evidence in complex external contexts.To address this issue, we examine the phenomenon of discrepancies in abilities across two distinct tasks-QA and evidence selection-when performed simultaneously, from the perspective of task alignment.To verify LLMs' task alignment, we introduce a verification framework and resources considering both semantic relevancy and structural diversity of the given long context knowledge.Through extensive experiments and detailed analysis, we provide insights into the task misalignment between QA and evidence selection.Our code and resources can be found at https://github.com/seonminkoo/WAI.