Improving Access to Justice with Legal Chatbots

Queudot, Marc; Charton, Éric; Meurs, Marie-Jean

doi:10.3390/stats3030023

Cited by 18 publications

(3 citation statements)

References 22 publications

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“…34 Tools like these can help provide a recommendation for the type of legal action needed to address the issue at hand or to recommend a specialized attorney. A number of other similar efforts have sought to increase access to justice by providing information tailored to a client's particular needs [Cabral et al 2012;Brescia et al 2014;Queudot et al 2020;Westermann et al 2019].…”

Section: Opportunities In Lawmentioning

confidence: 99%

On the Opportunities and Risks of Foundation Models

Bommasani¹,

Hudson²,

Adeli³

et al. 2021

Preprint

649

652

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AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical principles (e.g., model architectures, training procedures, data, systems, security, evaluation, theory) to their applications (e.g., law, healthcare, education) and societal impact (e.g., inequity, misuse, economic and environmental impact, legal and ethical considerations). Though foundation models are based on standard deep learning and transfer learning, their scale results in new emergent capabilities, and their effectiveness across so many tasks incentivizes homogenization. Homogenization provides powerful leverage but demands caution, as the defects of the foundation model are inherited by all the adapted models downstream. Despite the impending widespread deployment of foundation models, we currently lack a clear understanding of how they work, when they fail, and what they are even capable of due to their emergent properties. To tackle these questions, we believe much of the critical research on foundation models will require deep interdisciplinary collaboration commensurate with their fundamentally sociotechnical nature.

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Section: Opportunities In Lawmentioning

confidence: 99%

On the Opportunities and Risks of Foundation Models

Bommasani¹,

Hudson²,

Adeli³

et al. 2021

Preprint

649

652

View full text Add to dashboard Cite

show abstract

“…Modern systems such GPT-3 are already extremely proficient at producing accurate, insightful responses on almost any topic, with applications in diverse areas such as healthcare [3], legal advice chat bots [4] and providing feedback for student's work [5]. They are thus potentially capable of answering the short-form essay questions which form the basis of assessments in many university modules.…”

Section: Introductionmentioning

confidence: 99%

The death of the short-form physics essay in the coming AI revolution

et al. 2023

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The latest AI language modules can produce original, high quality full short-form (300-word) Physics essays within seconds. These technologies such as ChatGPT and davinci-003 are freely available to anyone with an internet connection. In this work, we present evidence of AI generated short-form essays achieving First-Class grades on an essay writing assessment from an accredited, current university Physics module. The assessment requires students answer five open-ended questions with a short, 300-word essay each. Fifty AI answers were generated to create ten submissions that were independently marked by five separate markers. The AI generated submissions achieved an average mark of 71 ± 2 % , in strong agreement with the current module average of 71 ± 5 % . A typical AI submission would therefore most-likely be awarded a First Class, the highest classification available at UK universities. Plagiarism detection software returned a plagiarism score between 2 ± 1 % (Grammarly) and 7 ± 2 % (TurnitIn). We argue that these results indicate that current natural language processing AI represent a significant threat to the fidelity of short-form essays as an assessment method in Physics courses.

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“…Dialog systems have reached a level of maturity that allows widespread application, leading to their development in various domains to meet different needs [1,2]. These systems communicate with users in natural language through text, speech or both.…”

Section: Introductionmentioning

confidence: 99%

Dataset Augmentation Using Back-Translation to Improve Early Stage Dialog Systems

Queudot¹,

Marceau²,

Belbahar³

et al. 2022

Proceedings of the Canadian Conference on Artificial Intelligence

Self Cite

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As dialog systems are increasingly used, a major challenge for building new ones is the lack of annotated training data. The necessary data collection and annotation efforts are laborious and time-consuming. A potential solution is to augment initial seed data by automatically paraphrasing existing samples. In this paper, we propose a novel dataefficient approach towards this goal. Our method can kick-start a dialog system with minimum human effort while delivering a performance strong enough to allow real-world usage. We ran experiments using Neural Machine Translation on two open corpora. On both of them, the proposed approach improved the generalization capabilities of the model. Our results suggest that paraphrase generation techniques could be used as-is to provide a boost in performance to dialog systems in an early phase.

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Improving Access to Justice with Legal Chatbots

Cited by 18 publications

References 22 publications

On the Opportunities and Risks of Foundation Models

On the Opportunities and Risks of Foundation Models

The death of the short-form physics essay in the coming AI revolution

Dataset Augmentation Using Back-Translation to Improve Early Stage Dialog Systems

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