Legal Judgment Prediction (LJP) focuses on predicting judgment results based on the facts of cases. While State-of-the-Art (SOTA) methods have shown impressive performance in law article prediction and charge prediction, they still exhibit weaknesses in prison term prediction. One major reason is that existing models fail to mimic human legal quantitative reasoning to understand monetary features in case facts. Consequently, they do not rigorously quantify the severity of the crime, which is essential for prison term prediction. In this article, we explore and explain how to leverage monetary features to improve LJP via quantitative reasoning. Specifically, we propose QR-LJP, a quantitative reasoning-based LJP model, to integrate legal reasoning knowledge into the prediction process. QR-LJP first employs a curated LLM to extract monetary values from case facts and uses legal quantitative reasoning logic to determine the total crime amount, serving as the quantitative measure of the crime’s severity. This measure is subsequently used to make judgment predictions. We evaluate our model on the real-world dataset CAIL-2018. Experimental results demonstrate that our model outperforms current SOTAs, highlighting the effectiveness of legal quantitative reasoning. Moreover, applying our quantitative reasoning strategy to existing SOTA methods yields significant improvements, especially in macro-F1 scores.

We introduce InternLM-Law, a large language model (LLM) tailored for addressing diverse legal tasks related to Chinese laws. These tasks range from responding to standard legal questions (e.g., legal exercises in textbooks) to analyzing complex real-world legal situations. Our work contributes to Chinese Legal NLP research by (1) conducting one of the most extensive evaluations of state-of-the-art general-purpose and legal-specific LLMs to date that involves an automatic evaluation on the 20 legal NLP tasks in LawBench, a human evaluation on a challenging version of the Legal Consultation task, and an automatic evaluation of a model’s ability to handle very long legal texts; (2) presenting a methodology for training a Chinese legal LLM that offers superior performance to all of its counterparts in our extensive evaluation; and (3) facilitating future research in this area by making all of our code and model publicly available at https://github.com/InternLM/InternLM-Law.

Large language models (LLMs) achieve remarkable performance across tasks but incur substantial computational costs due to their deep, multi-layered architectures. Layer pruning has emerged as a strategy to alleviate these inefficiencies, but conventional static pruning methods overlook two critical dynamics inherent to LLM inference: (1) *horizontal dynamics*, where token-level heterogeneity demands context-aware pruning decisions, and (2) *vertical dynamics*, where the distinct functional roles of MLP and self-attention layers necessitate component-specific pruning policies. We introduce **SkipGPT**, a dynamic layer pruning framework designed to optimize computational resource allocation through two core innovations: (1) global token-aware routing to prioritize critical tokens and (2) decoupled pruning policies for MLP and self-attention components. To mitigate training instability, we propose a two-stage optimization paradigm: first, a disentangled training phase that learns routing strategies via soft parameterization to avoid premature pruning decisions, followed by parameter-efficient LoRA fine-tuning to restore performance impacted by layer removal. Extensive experiments demonstrate that SkipGPT reduces over 40% model parameters while matching or exceeding the performance of the original dense model across benchmarks. By harmonizing dynamic efficiency with preserved expressivity, SkipGPT advances the practical deployment of scalable, resource-aware LLMs. Our code is publicly available at: https://github.com/EIT-NLP/SkipGPT.

Most automated program repair methods rely on test cases to determine the correctness of the generated patches. However, due to the incompleteness of available test suites, some patches that pass all the test cases may still be incorrect. This issue is known as the patch overfitting problem. Overfitting problem is a longstanding problem in automated program repair. Due to overfitting patches, the patches obtained by automated program repair tools require further validation to determine their correctness. Researchers have proposed many methods to automatically assess the correctness of patches, but no systematic review provides a detailed introduction to this problem, the existing solutions, and the challenges. To address this deficiency, we systematically review the existing approaches to patch correctness assessment. We first offer a few examples of overfitting patches to acquire a more detailed understanding of this problem. We then propose a comprehensive categorization of publicly available techniques and datasets, examine the commonly used evaluation metrics, and perform an in-depth analysis of the effectiveness of the existing models in addressing the challenge of overfitting. Based on our analysis, we provided the difficulties encountered by current methodologies, alongside the possible avenues for future research exploration.

Zhiwei Fei, Xiaoyu Shen, Dawei Zhu, Fengzhe Zhou, Zhuo Han, Alan Huang, Songyang Zhang, Kai Chen, Zhixin Yin, Zongwen Shen, Jidong Ge, Vincent Ng. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024.