Text Retrieval for Language Learners: Graded Vocabulary vs. Open Learner Model

Lee, John; Yeung, Chak Yan

doi:10.26615/978-954-452-072-4_091

Cited by 2 publications

(1 citation statement)

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“…The use of artificial intelligence (AI) and machine learning in the legal field has attracted significant attention in recent years, resulting in the development of numerous state-ofthe-art models capable of performing various legal text processing tasks, such as contract analysis and case law retrieval [3,4,5,6]. Deep learning models, which offer automated feature extraction, have been utilized not only in similarity matching tasks but also in other semantic matching tasks such as question answering [7,8], machine reading comprehension [9,10], image retrieval [11], and entity matching [12,13]. Thus, these models have proven valuable in the legal domain, where accurate information retrieval and abductive reasoning skills are essential.…”

Section: Background and Related Workmentioning

confidence: 99%

LogiLaw Dataset Towards Reinforcement Learning from Logical Feedback (RLLF)

Nguyen,

Fungwacharakorn,

Satoh

2023

Frontiers in Artificial Intelligence and Applications

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Large Language Models (LLMs) face limitations in logical reasoning, which restrict their applicability in critical domains such as law. Current evaluation methods often lead to inaccurate assessments of LLMs’ capabilities due to their simplicity. This paper presents a refined evaluation method for assessing LLMs’ capability to answer legal questions by eliminating the possibility of obtaining correct responses by chance. Furthermore, we introduce the LogiLaw dataset, which aims to enhance the models’ logical reasoning capacities in general and legal reasoning specifically. By leveraging the refined evaluation technique, the LogiLaw dataset, and the proposed Reinforcement Learning from Logical Feedback (RLLF) approach, our work aims to open new avenues for research to bolster LLMs’ performance in law and other logic-intensive disciplines while addressing the shortcomings of conventional evaluation approaches.

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