Zobrazeno 1 - 10
of 340
pro vyhledávání: '"Satoh, Ken"'
This paper presents a novel approach termed Layer-of-Thoughts Prompting (LoT), which utilizes constraint hierarchies to filter and refine candidate responses to a given query. By integrating these constraints, our method enables a structured retrieva
Externí odkaz:
http://arxiv.org/abs/2410.12153
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
Reasoning is an essential component of human intelligence as it plays a fundamental role in our ability to think critically, support responsible decisions, and solve challenging problems. Traditionally, AI has addressed reasoning in the context of lo
Externí odkaz:
http://arxiv.org/abs/2410.05339
Although various aspects of soft-constraint based norms have been explored, it is still challenging to understand preemption. Preemption is a situation where higher-level norms override lower-level norms when new information emerges. To address this,
Externí odkaz:
http://arxiv.org/abs/2409.04065
In this paper, we explore the application of Generative Pre-trained Transformers (GPTs) in cross-lingual legal Question-Answering (QA) systems using the COLIEE Task 4 dataset. In the COLIEE Task 4, given a statement and a set of related legal article
Externí odkaz:
http://arxiv.org/abs/2403.18098
Autor:
Nguyen, Hai-Long, Nguyen, Duc-Minh, Nguyen, Tan-Minh, Nguyen, Ha-Thanh, Vuong, Thi-Hai-Yen, Satoh, Ken
Large language models with billions of parameters, such as GPT-3.5, GPT-4, and LLaMA, are increasingly prevalent. Numerous studies have explored effective prompting techniques to harness the power of these LLMs for various research problems. Retrieva
Externí odkaz:
http://arxiv.org/abs/2403.18093
Autor:
Nguyen, Ha-Thanh, Satoh, Ken
Finetuning approaches in NLP often focus on exploitation rather than exploration, which may lead to suboptimal models. Given the vast search space of natural language, this limited exploration can restrict their performance in complex, high-stakes do
Externí odkaz:
http://arxiv.org/abs/2403.01185
This paper addresses the problem of constructing a policy pipeline that enables compliance checking of business processes against regulatory obligations. Towards this end, we propose an Open Digital Rights Language (ODRL) profile that can be used to
Externí odkaz:
http://epub.wu.ac.at/7078/1/RuleML%2BRR_2019_Final.pdf
Language serves as a vehicle for conveying thought, enabling communication among individuals. The ability to distinguish between diverse concepts, identify fairness and injustice, and comprehend a range of legal notions fundamentally relies on logica
Externí odkaz:
http://arxiv.org/abs/2311.13095
We present our method for tackling a legal case retrieval task by introducing our method of encoding documents by summarizing them into continuous vector space via our phrase scoring framework utilizing deep neural networks. On the other hand, we exp
Externí odkaz:
http://arxiv.org/abs/2309.08187