The paper defines a weight-matrix similarity index based on maximum absolute cosine values and Gumbel fitting, then uses it to show that neighboring transformer layers in open LLMs have similar weights and form clusters.
NeCo@ALQAC 2023: Legal Domain Knowledge Acquisition for Low-Resource Languages through Data Enrichment
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
In recent years, natural language processing has gained significant popularity in various sectors, including the legal domain. This paper presents NeCo Team's solutions to the Vietnamese text processing tasks provided in the Automated Legal Question Answering Competition 2023 (ALQAC 2023), focusing on legal domain knowledge acquisition for low-resource languages through data enrichment. Our methods for the legal document retrieval task employ a combination of similarity ranking and deep learning models, while for the second task, which requires extracting an answer from a relevant legal article in response to a question, we propose a range of adaptive techniques to handle different question types. Our approaches achieve outstanding results on both tasks of the competition, demonstrating the potential benefits and effectiveness of question answering systems in the legal field, particularly for low-resource languages.
fields
cs.CL 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models
The paper defines a weight-matrix similarity index based on maximum absolute cosine values and Gumbel fitting, then uses it to show that neighboring transformer layers in open LLMs have similar weights and form clusters.