A Brief Report on LawGPT 1.0: A Virtual Legal Assistant Based on GPT-3

Nguyen, Ha-Thanh

doi:10.48550/arxiv.2302.05729

Cited by 5 publications

(5 citation statements)

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“…Among many open-source LLMs, LLAMA [27] has achieved excellent predictive results by pre-training on different domain datasets, with its performance in some benchmark tests even surpassing the current teacher-leveled GPT-3 model. Many researchers have further fine-tuned and expanded various professional LLM based on this model, such as TransGPT [29], MetaMath [29], LawGPT [30], and others. Recently, Meta has released LLAMA2 [31], which is free for commercial use and has shown further performance improvement compared to its predecessor.…”

Section: Llm-based Named Entity Recognition and Entity Linkingmentioning

confidence: 99%

Material Property Prediction with Joint Reasoning based on Large Language Models and Knowledge Graphs for Lithium Batteries

Lei,

Yin,

Xiao

et al. 2024

Preprint

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Lithium batteries, as a crucial part of modern energy storage, rely heavily on the properties of their materials, which affect energy density, cycle life, charge/dis-charge rates, and safety. Traditional experimental methods for predicting these properties are often costly and time-consuming. While data-driven machine learning approaches can predict performance by analyzing the impact of various factors, inconsistencies in data measurement and reporting, along with a lack of semantic integration of material information, hinder research on lithium battery materials. This paper introduces a lithium battery material property prediction method based on joint reasoning with large language models and knowledge graphs (JRLKG). Knowledge relationships are extracted from extensive literature to construct a knowledge graph encompassing material structure, properties, and research methods. By assessing material similarity within the graph, related information is fed into the GPT-4 model. Prompt learning techniques guide GPT-4 to leverage explicit knowledge from the external knowledge graph and its implicit knowledge for joint reasoning predictions, providing results and rationales. Experiments show that JRLKG improves the accuracy and inter-pretability of material property predictions, offering new avenues and methods for research and shortening the Research and development cycle for lithium-ion battery materials.

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Section: Llm-based Named Entity Recognition and Entity Linkingmentioning

confidence: 99%

Material Property Prediction with Joint Reasoning based on Large Language Models and Knowledge Graphs for Lithium Batteries

Lei,

Yin,

Xiao

et al. 2024

Preprint

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“…They studied approximately 8 representative legal data sets, with sample sizes ranging from 900 to 57 K. These data sets covered a spectrum consisting of five NLP tasks, encompassing binary classification, multilabel classification, multiple-choice Q&A, summarization and information retrieval. Nguyen (2023) designed a virtual legal assistant built on a version of the GPT-3 language model that was fine-tuned for the legal domain. The system was designed to provide legal assistance to users in a conversational manner, helping them with tasks such as answering legal questions, generating legal documents and providing legal advice.…”

Section: 3mentioning

confidence: 99%

Large language models for automated Q&A involving legal documents: a survey on algorithms, frameworks and applications

Yang,

Wang,

Wang

et al. 2024

IJWIS

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Purpose This study aims to adopt a systematic review approach to examine the existing literature on law and LLMs.It involves analyzing and synthesizing relevant research papers, reports and scholarly articles that discuss the use of LLMs in the legal domain. The review encompasses various aspects, including an analysis of LLMs, legal natural language processing (NLP), model tuning techniques, data processing strategies and frameworks for addressing the challenges associated with legal question-and-answer (Q&A) systems. Additionally, the study explores potential applications and services that can benefit from the integration of LLMs in the field of intelligent justice. Design/methodology/approach This paper surveys the state-of-the-art research on law LLMs and their application in the field of intelligent justice. The study aims to identify the challenges associated with developing Q&A systems based on LLMs and explores potential directions for future research and development. The ultimate goal is to contribute to the advancement of intelligent justice by effectively leveraging LLMs. Findings To effectively apply a law LLM, systematic research on LLM, legal NLP and model adjustment technology is required. Originality/value This study contributes to the field of intelligent justice by providing a comprehensive review of the current state of research on law LLMs.

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“…The recent works explored GLLMs for a variety of legal NLP tasks like natural language inference [344], question answering [188], [347], [352], text generation [345], [347] and text classification [346]. Table 14 presents a summary of research works exploring GLLMs for various NLP tasks in the legal domain.…”

Section: Legal Domainmentioning

confidence: 99%

“…However, the model performance is almost 18% less than the human performance, and overall model performance is below the passing threshold. Nguyen et al [345] presented LawGPT 1.0, the first-ever chatbot model based on GPT-3 for the legal domain. The GPT-3 model is pretrained on mostly generic corpus, so it lacks domain-specific knowledge.…”

Section: Legal Domainmentioning

confidence: 99%

A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4

Kalyan

2023

SSRN Journal

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A Brief Report on LawGPT 1.0: A Virtual Legal Assistant Based on GPT-3

Cited by 5 publications

References 0 publications

Material Property Prediction with Joint Reasoning based on Large Language Models and Knowledge Graphs for Lithium Batteries

Material Property Prediction with Joint Reasoning based on Large Language Models and Knowledge Graphs for Lithium Batteries

Large language models for automated Q&A involving legal documents: a survey on algorithms, frameworks and applications

A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4

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