Zobrazeno 1 - 10
of 15 804
pro vyhledávání: '"Legal domain"'
Autor:
Ariai, Farid, Demartini, Gianluca
Natural Language Processing is revolutionizing the way legal professionals and laypersons operate in the legal field. The considerable potential for Natural Language Processing in the legal sector, especially in developing computational tools for var
Externí odkaz:
http://arxiv.org/abs/2410.21306
Autor:
Thanh, Nguyen Ha, Satoh, Ken
This paper introduces Knowledge Representation Augmented Generation (KRAG), a novel framework designed to enhance the capabilities of Large Language Models (LLMs) within domain-specific applications. KRAG points to the strategic inclusion of critical
Externí odkaz:
http://arxiv.org/abs/2410.07551
Autor:
Pipitone, Nicholas, Alami, Ghita Houir
Retrieval-Augmented Generation (RAG) systems are showing promising potential, and are becoming increasingly relevant in AI-powered legal applications. Existing benchmarks, such as LegalBench, assess the generative capabilities of Large Language Model
Externí odkaz:
http://arxiv.org/abs/2408.10343
Autor:
Hamdani, Rajaa El, Bonald, Thomas, Malliaros, Fragkiskos, Holzenberger, Nils, Suchanek, Fabian
This paper investigates the factuality of large language models (LLMs) as knowledge bases in the legal domain, in a realistic usage scenario: we allow for acceptable variations in the answer, and let the model abstain from answering when uncertain. F
Externí odkaz:
http://arxiv.org/abs/2409.11798
The legal landscape encompasses a wide array of lawsuit types, presenting lawyers with challenges in delivering timely and accurate information to clients, particularly concerning critical aspects like potential imprisonment duration or financial rep
Externí odkaz:
http://arxiv.org/abs/2407.19041
Hybrid search has emerged as an effective strategy to offset the limitations of different matching paradigms, especially in out-of-domain contexts where notable improvements in retrieval quality have been observed. However, existing research predomin
Externí odkaz:
http://arxiv.org/abs/2409.01357
Autor:
Colombo, Pierre, Pires, Telmo, Boudiaf, Malik, Melo, Rui, Culver, Dominic, Morgado, Sofia, Malaboeuf, Etienne, Hautreux, Gabriel, Charpentier, Johanne, Desa, Michael
In this paper, we introduce SaulLM-54B and SaulLM-141B, two large language models (LLMs) tailored for the legal sector. These models, which feature architectures of 54 billion and 141 billion parameters, respectively, are based on the Mixtral archite
Externí odkaz:
http://arxiv.org/abs/2407.19584
In recent years, the field of Legal Tech has risen in prevalence, as the Natural Language Processing (NLP) and legal disciplines have combined forces to digitalize legal processes. Amidst the steady flow of research solutions stemming from the NLP do
Externí odkaz:
http://arxiv.org/abs/2404.18759
Autor:
Niyogi, Mitodru, Bhattacharya, Arnab
In this paper, we present Paramanu-Ayn, a collection of legal language models trained exclusively on Indian legal case documents. This 97-million-parameter Auto-Regressive (AR) decoder-only model was pretrained from scratch with a context size of 819
Externí odkaz:
http://arxiv.org/abs/2403.13681
Autor:
Tripathi, Yogesh, Donakanti, Raghav, Girhepuje, Sahil, Kavathekar, Ishan, Vedula, Bhaskara Hanuma, Krishnan, Gokul S, Goyal, Shreya, Goel, Anmol, Ravindran, Balaraman, Kumaraguru, Ponnurangam
Recent advancements in language technology and Artificial Intelligence have resulted in numerous Language Models being proposed to perform various tasks in the legal domain ranging from predicting judgments to generating summaries. Despite their imme
Externí odkaz:
http://arxiv.org/abs/2402.10567