Large language models (LLMs) are increasingly used as evaluators in natural language generation tasks, offering advantages in scalability and interpretability over traditional evaluation methods.However, existing LLMbased evaluations often suffer from biases and misalignment, particularly in domain-specific tasks, due to limited functional understanding and knowledge gaps.To address these challenges, we first investigate the relationship between an LLM-based evaluator's familiarity with the target task and its evaluation performance.We then introduce the Co-Eval framework, which leverages a criteria planner model and optimized machine metrics to enhance the scalability and fairness of LLMbased evaluation.Experimental results on both general and domain-specific tasks demonstrate that Co-Eval reduces biases, achieving up to a 0.4903 reduction in self-preference bias, and improves alignment with human preferences, with gains of up to 0.324 in Spearman correlation.

Minyu Chen, Guoqiang Li, Ling-I Wu, Ruibang Liu. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

Lightweight robots are frequently engaged in real-time tracking tasks to provide human companionship services. For effective target tracking, the YOLO series is often employed as a lightweight object detection framework in robot systems. However, YOLO still demands substantial resources to train larger-scale models, striking a balance between accuracy and resource efficiency. Deploying YOLO directly on robots with limited computing resources can lead to significant delays in detection, compromising the effectiveness of tracking tasks. A deeper concern arises from the prevalent use of CPUs as the primary computing units in robots, rendering many existing model optimization techniques, which primarily target GPU computing, unsuitable for this context.

Open-source platforms such as GitHub and Stack Overflow both play significant roles in current software ecosystems. It is crucial but time-consuming for developers to raise programming questions in coding forums such as Stack Overflow and be navigated to actual solutions on GitHub repositories. In this paper, we dedicate to accelerating this activity. We find that traditional information retrieval-based methods fail to handle the long and complex questions in coding forums, and thus cannot find suitable coding repositories. To effectively and efficiently bridge the semantic gap between repositories and real-world coding questions, we introduce a specialized dataset named Repo4QA, which includes over 12,000 question-repository pairs constructed from Stack Overflow and GitHub. Furthermore, we propose QuRep, a CodeBERT-based model that jointly learns the representation of both questions and repositories. Experimental results demonstrate that our model simultaneously captures the semantic features in both questions and repositories through supervised contrastive loss and hard negative sampling. We report that our approach outperforms existing state-of-art methods by 3%-8% on MRR and 5%-8% on P@1.