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The Unreasonable Effectiveness Of Large Language Models In Zero-Shot Semantic Annotation Of Legal Texts, Jaromir Savelka, Kevin D. Ashley
The Unreasonable Effectiveness Of Large Language Models In Zero-Shot Semantic Annotation Of Legal Texts, Jaromir Savelka, Kevin D. Ashley
Articles
The emergence of ChatGPT has sensitized the general public, including the legal profession, to large language models' (LLMs) potential uses (e.g., document drafting, question answering, and summarization). Although recent studies have shown how well the technology performs in diverse semantic annotation tasks focused on legal texts, an influx of newer, more capable (GPT-4) or cost-effective (GPT-3.5-turbo) models requires another analysis. This paper addresses recent developments in the ability of LLMs to semantically annotate legal texts in zero-shot learning settings. Given the transition to mature generative AI systems, we examine the performance of GPT-4 and GPT-3.5-turbo(-16k), comparing it to the previous …
Walking The Walk: Ex-Prisoners, Lived Experience, And The Delivery Of Restorative Justice, Allely Albert
Walking The Walk: Ex-Prisoners, Lived Experience, And The Delivery Of Restorative Justice, Allely Albert
Articles
Although the role of prisoners and ex-prisoners has recently received significant attention in restorative justice research, the literature typically treats them as the ‘offending’ party within restorative justice processes. This article instead focuses on ex-prisoners as facilitators of restorative justice, highlighting their ability to lead such programmes. Using a case study from Northern Ireland, the article examines the way that experiences of incarceration have directly influenced practitioners’ skills and their ability to uphold restorative justice principles. It is contended that qualities developed and honed in the prison environment ultimately translate to unique characteristics that can improve the restorative process. As …