Worked on the HuanzhiMao/gorilla repository to address a critical data-quality issue affecting multi-turn evaluation metrics on the xpander.ai platform. Using Python and leveraging skills in data correction and machine learning evaluation, implemented a targeted fix that corrected a ground truth typo in the multi-turn base evaluation data. This patch improved the accuracy and reliability of performance metrics, ensuring that model evaluations reflect true conversational outcomes. The work involved careful analysis of evaluation data and integration with existing repository documentation, directly resolving issue #956. The update enhanced the integrity of multi-turn performance assessments without introducing new features or architectural changes.