Outside OpenAI's headquarters, a handful of people gathered on Monday holding pieces of colorful chalk. They got down on their knees and started writing messages on the sidewalk. Please no legal mass surveillance. At issue was a business deal that the company recently signed with the Department of Defense, following the Pentagon's sudden turn against Anthropic . OpenAI will now supply its technology to the military for use in classified settings, the sorts that may involve wartime decisions and intelligence-gathering--an agreement, many legal experts told me, that could give the government wide-ranging powers.

Several legal experts have told BBC Verify that the second strike on an alleged Venezuelan drug boat by the US military was probably illegal, and would likely be considered an extrajudicial killing under international law. On Monday, the Trump administration confirmed that a follow-up strike on the boat - which has been criticised as a double tap - was ordered by US Navy Admiral Frank Bradley with the overall operation having been authorised by War Secretary Pete Hegseth. Nine people died in the first strike on the vessel and two survivors were left clinging to the burning wreckage when it was struck again, killing them, according to the Washington Post. A US official has said four missiles were used in the operation. The Trump administration has not denied there were survivors and has insisted the strikes on 2 September were in accordance with the law of armed conflict.

After launching in October in the US and Canada via invitation only, OpenAI's video app, Sora 2, hit 1m downloads in just five days. After launching in October in the US and Canada via invitation only, OpenAI's video app, Sora 2, hit 1m downloads in just five days. The video app can produce realistic deepfakes of Marx shopping and MLK Jr trolling. Some say using'historical figures' is the company's way of testing the legal waters L ast night I was flicking through a dating app. One guy stood out: "Henry VIII, 34, King of England, nonmonogamy".

Large language models (LLMs) have made significant progress in natural language processing tasks and demonstrate considerable potential in the legal domain.

Access to consumer grievance redressal in India is often hindered by procedural complexity, legal jargon, and jurisdictional challenges. To address this, we present $\textbf{Grahak-Nyay}$ (Justice-to-Consumers), a chatbot that streamlines the process using open-source Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG). Grahak-Nyay simplifies legal complexities through a concise and up-to-date knowledge base. We introduce three novel datasets: $\textit{GeneralQA}$ (general consumer law), $\textit{SectoralQA}$ (sector-specific knowledge) and $\textit{SyntheticQA}$ (for RAG evaluation), along with $\textit{NyayChat}$, a dataset of 300 annotated chatbot conversations. We also introduce $\textit{Judgments}$ data sourced from Indian Consumer Courts to aid the chatbot in decision making and to enhance user trust. We also propose $\textbf{HAB}$ metrics ($\textbf{Helpfulness, Accuracy, Brevity}$) to evaluate chatbot performance. Legal domain experts validated Grahak-Nyay's effectiveness. Code and datasets will be released.

The automated summarisation of long legal documents can be a great aid for legal experts in their daily work. We automatically create summaries (guiding principles) of German judgments by fine-tuning a decoder-based large language model. We enrich the judgments with information about legal entities before the training. For the evaluation of the created summaries, we define a set of evaluation classes which allows us to measure their language, pertinence, completeness and correctness. Our results show that employing legal entities helps the generative model to find the relevant content, but the quality of the created summaries is not yet sufficient for a use in practice.

The integration of language models in the legal domain holds considerable promise for streamlining processes and improving efficiency in managing extensive workloads. However, the specialized terminology, nuanced language, and formal style of legal texts can present substantial challenges. This study examines whether preference-based training techniques, specifically Reinforcement Learning from Human Feedback and Direct Preference Optimization, can enhance models' performance in generating Icelandic legal summaries that align with domain-specific language standards and user preferences. We compare models fine-tuned with preference training to those using conventional supervised learning. Results indicate that preference training improves the legal accuracy of generated summaries over standard fine-tuning but does not significantly enhance the overall quality of Icelandic language usage. Discrepancies between automated metrics and human evaluations further underscore the importance of qualitative assessment in developing language models for the legal domain.