RocketEval: Efficient Automated LLM Evaluation via Grading Checklist

Evaluating large language models (LLMs) in diverse and challenging scenarios is essential to align them with human preferences. To mitigate the prohibitive costs associated with human evaluations, utilizing a powerful LLM as a judge has emerged as a favored approach. Nevertheless, this methodology encounters several challenges, including substantial expenses, concerns regarding privacy and security, and reproducibility. In this paper, we propose a straightforward, replicable, and accurate automated evaluation method by leveraging a lightweight LLM as the judge, named RocketEval. Initially, we identify that the performance disparity between lightweight and powerful LLMs in evaluation tasks primarily stems from their ability to conduct comprehensive analyses, which is not easily enhanced through techniques such as chain-of-thought reasoning. By reframing the evaluation task as a multi-faceted Q&A using an instance-specific checklist, we demonstrate that the limited judgment accuracy of lightweight LLMs is largely attributes to high uncertainty and positional bias. To address these challenges, we introduce an automated evaluation process grounded in checklist grading, which is designed to accommodate a variety of scenarios and questions. This process encompasses the creation of checklists, the grading of these checklists by lightweight LLMs, and the reweighting of checklist items to align with the supervised annotations. Our experiments carried out on the automated evaluation benchmarks, MT-Bench and WildBench datasets, reveal that RocketEval, when using Gemma-2-2B as the judge, achieves a high correlation (0.965) with human preferences, which is comparable to GPT-4o. Moreover, RocketEval provides a cost reduction exceeding 50-fold for large-scale evaluation and comparison scenarios. Our code is available at https://github.com/Joinn99/RocketEval-ICLR .

Collaborative Residual Metric Learning

In collaborative filtering, distance metric learning has been applied to matrix factorization techniques with promising results. However, matrix factorization lacks the ability of capturing collaborative information, which has been remarked by recent works and improved by interpreting user interactions as signals. This paper aims to find out how metric learning connect to these signal-based models. By adopting a generalized distance metric, we discovered that in signal-based models, it is easier to estimate the residual of distances, which refers to the difference between the distances from a user to a target item and another item, rather than estimating the distances themselves. Further analysis also uncovers a link between the normalization strength of interaction signals and the novelty of recommendation, which has been overlooked by existing studies. Based on the above findings, we propose a novel model to learn a generalized distance user-item distance metric to capture user preference in interaction signals by modeling the residuals of distance. The proposed CoRML model is then further improved in training efficiency by a newly introduced approximated ranking weight. Extensive experiments conducted on 4 public datasets demonstrate the superior performance of CoRML compared to the state-of-the-art baselines in collaborative filtering, along with high efficiency and the ability of providing novelty-promoted recommendations, shedding new light on the study of metric learning-based recommender systems.

Efficient and Scalable Recommendation via Item-Item Graph Partitioning

Collaborative filtering (CF) is a widely searched problem in recommender systems. Linear autoencoder is a kind of well-established method for CF, which estimates item-item relations through encoding user-item interactions. Despite the excellent performance of linear autoencoders, the rapidly increasing computational and storage costs caused by the growing number of items limit their scalabilities in large-scale real-world scenarios. Recently, graph-based approaches have achieved success on CF with high scalability, and have been shown to have commonalities with linear autoencoders in user-item interaction modeling. Motivated by this, we propose an efficient and scalable recommendation via item-item graph partitioning (ERGP), aiming to address the limitations of linear autoencoders. In particular, a recursive graph partitioning strategy is proposed to ensure that the item set is divided into several partitions of finite size. Linear autoencoders encode user-item interactions within partitions while preserving global information across the entire item set. This allows ERGP to have guaranteed efficiency and high scalability when the number of items increases. Experiments conducted on 3 public datasets and 3 open benchmarking datasets demonstrate the effectiveness of ERGP, which outperforms state-of-the-art models with lower training time and storage costs.