H.3.8. Natural Language Processing
Hassan Deldar; Mohammad Mehdi Homayounpour
Abstract
In most of the countries, the legislative process has a long history, which has led to increasing diversity and multiplicity of laws. This has made it difficult to access laws that are valid in both time and place. The focus of this article is on the application of artificial intelligence in the domain ...
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In most of the countries, the legislative process has a long history, which has led to increasing diversity and multiplicity of laws. This has made it difficult to access laws that are valid in both time and place. The focus of this article is on the application of artificial intelligence in the domain of legal statutes to assist in identifying the need for amendments to laws or specific provisions. The general framework of the proposed process consists of two key components.First, the texts of legal clauses or articles are enriched through the generation of enriched data using large language models, which involves producing embedding vectors, thematic classification,and extracting the provisions of each law. Second, a retrieval-augmented text generation (RAG) system is developed with the aid of large language models to determine conflicts or the need for expurgation in the output, utilizing the enriched data, predefined prompts, and the Chain of Thought (CoT) technique.The proposed method was evaluated on two benchmark datasets.On the COLIEE 2025 dataset, our approach outperformed the 2024 winners in legal implication tasks, achieving an F1 score of 0.6521 with minimal prompting. The second evaluation used over 1,000 legal clauses covering abrogation and neutral rules, yielding an impressive F1 score exceeding 73.41%.The findings of the proposed methodology demonstrate that, even with limited expertise in the legal domain, it is possible to identify conflicts and the necessity for refining legal texts to an acceptable degree within a reasonable timeframe for legal experts, leveraging the capabilities of large language models.
H.3.8. Natural Language Processing
Saedeh Tahery; Saeed Farzi
Abstract
Dialogue understanding for low-resource languages like Persian remains challenging due to limited annotated data, which constrains supervised training at scale. We propose a simple yet effective training-free method that combines machine translation, retrieval-based example selection, and prompting with ...
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Dialogue understanding for low-resource languages like Persian remains challenging due to limited annotated data, which constrains supervised training at scale. We propose a simple yet effective training-free method that combines machine translation, retrieval-based example selection, and prompting with a large language model (GPT-4o) to improve zero-shot cross-lingual performance. Given a Persian utterance translated into English, our method retrieves semantically and lexically similar English examples using a hybrid similarity function, translates them back into Persian, and constructs a few-shot prompt tailored to the input. This input-sensitive strategy enhances the quality of the examples, helping the model align more effectively with each instance. Experimental results on the Persian-ATIS dataset show that our approach improves intent detection and achieves competitive slot filling performance, outperforming state-of-the-art baselines without requiring any supervision in the target language. The modular pipeline is easy to reproduce and, in future work, can be extended to other low-resource languages, tasks, or retrieval configurations. The repository of our work is available at https://anonymous.4open.science/r/Persian_Language_Understanding-FDF4.
