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Bin Wei

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3 papers

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AILAW Journal 2025 Journal Article

An LLMs-based neuro-symbolic legal judgment prediction framework for civil cases

Bin Wei
Yaoyao Yu
Leilei Gan
Fei Wu

Abstract In recent years, the field of AI & Law has increasingly focused on predicting legal judgments, particularly in civil cases. While traditional neural network methods are highly effective at automatically learning patterns from large datasets, they often suffer from a lack of interpretability. To address this limitation, we propose a neuro-symbolic framework for legal judgment prediction, based on large language models (LLMs). This framework combines legal knowledge (e. g. , legal rules), represented through first-order logic rules, with deep neural networks (DNNs), using a discrepancy loss to minimize prediction differences between the two components. By integrating the logic module during end-to-end training, knowledge is effectively transferred to the model parameters. Additionally, we develop a Chain-of-Thought prompt that uses LLMs to extract fact elements from legal cases. These elements act as logical variables within the rules, supporting the reasoning process in the logic module and improving overall interpretability. To validate the effectiveness of this framework, we conduct extensive experiments on a large dataset of private lending cases. The results demonstrate that the framework not only improves predictive performance but also enhances the interpretability of judgment predictions.

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AILAW Journal 2025 Journal Article

Specialized or general AI? a comparative evaluation of LLMs’ performance in legal tasks

Xue Guo
Yuting Huang
Bin Wei
Kun Kuang
Yiquan Wu
Leilei Gan
Xianshan Huang
Xianglin Dong

Abstract The rise of large language models (LLMs) such as ChatGPT and GPT-4 developed by OpenAI have generated significant interest in the legal domain due to their sophisticated language processing capabilities. In particular, regions like China are vigorously developing legal-specific LLMs for legal purposes. Fine-tuned with fewer parameters and based on judicial documents and Chinese case data sets, these specialized LLMs are widely expected to meet practical needs in the judicial field more effectively. However, the ability of these law-specific LLMs to perform legal tasks and their potential to outperform general LLMs has not yet been established. To fill in this research gap, we systematically evaluate a range of general and legal-specific LLMs on various legal tasks. The results show that GPT-4 maintains superior performance on most legal tasks, although legal-specific LLMs show superior performance in specific cases. This study provides insight into the factors leading to these results, hoping to enrich the discourse on the use of LLMs in the legal field.

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AAAI Conference 2011 Conference Paper

Heterogeneous Transfer Learning with RBMs

Bin Wei
Christopher Pal

A common approach in machine learning is to use a large amount of labeled data to train a model. Usually this model can then only be used to classify data in the same feature space. However, labeled data is often expensive to obtain. A number of strategies have been developed by the machine learning community in recent years to address this problem, including: semi-supervised learning, domain adaptation, multi-task learning, and self-taught learning. While training data and test may have different distributions, they must remain in the same feature set. Furthermore, all the above methods work in the same feature space. In this paper, we consider an extreme case of transfer learning called heterogeneous transfer learning - where the feature spaces of the source task and the target tasks are disjoint. Previous approaches mostly fall in the multi-view learning category, where cooccurrence data from both feature spaces is required. We generalize the previous work on cross-lingual adaptation and propose a multi-task strategy for the task. We also propose the use of a restricted Boltzmann machine (RBM), a special type of probabilistic graphical models, as an implementation. We present experiments on two tasks: action recognition and cross-lingual sentiment classiﬁcation.

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