Litigation

arXiv:2603.05828v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated remarkable performance in text generation and knowledge-intensive question answering. Nevertheless, they are prone to producing hallucinated content, which severely undermines their reliability in high-stakes application domains. Existing hallucination attribution...

arXiv:2603.05890v1 Announce Type: new Abstract: What happens when a storyteller forgets its own story? Large Language Models (LLMs) can now generate narratives spanning tens of thousands of words, but they often fail to maintain consistency throughout. When generating long-form narratives,...

arXiv:2603.05909v1 Announce Type: new Abstract: LLMs are increasingly deployed in high-stakes domains such as medical triage and legal assistance, often as document-grounded QA systems in which a user provides a description, relevant sources are retrieved, and an LLM generates a...

arXiv:2603.06114v1 Announce Type: new Abstract: Real-world arguments in text and dialogues are normally enthymemes (i.e. some of their premises and/or claims are implicit). Natural language processing (NLP) methods for handling enthymemes can potentially identify enthymemes in text but they do...

arXiv:2603.06123v1 Announce Type: new Abstract: Unlike autoregressive language models, which terminate variable-length generation upon predicting an End-of-Sequence (EoS) token, Diffusion Language Models (DLMs) operate over a fixed maximum-length context window for a predetermined number of denoising steps. However, this process...

arXiv:2603.06194v1 Announce Type: new Abstract: Subjective multi-turn dialogue tasks, such as emotional support, require conversational policies that adapt to evolving user states and optimize long-horizon interaction quality. However, reinforcement learning (RL) for such settings remains challenging due to the absence...

arXiv:2603.06198v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) is a framework in which a Generator, such as a Large Language Model (LLM), produces answers by retrieving documents from an external collection using a Retriever. In practice, Generators must integrate evidence...

arXiv:2603.06199v1 Announce Type: new Abstract: Long-context modeling is a pivotal capability for Large Language Models, yet the quadratic complexity of attention remains a critical bottleneck, particularly during the compute-intensive prefilling phase. While various sparse attention mechanisms have been explored, they...

arXiv:2603.06324v1 Announce Type: new Abstract: This study explores artificial visual creativity, focusing on ChatGPT's ability to generate new images intentionally pastiching original artworks such as paintings, drawings, sculptures and installations. The process involved twelve artists from Romania, Bulgaria, France, Austria,...

arXiv:2603.06348v1 Announce Type: new Abstract: Mathematical text understanding is a challenging task due to the presence of specialized entities and complex relationships between them. This study formulates mathematical problem interpretation as a Mathematical Entity Relation Extraction (MERE) task, where operands...

arXiv:2603.06416v1 Announce Type: new Abstract: As large language models (LLMs) advance in linguistic competence, their reasoning abilities are gaining increasing attention. In humans, reasoning often performs well in domain specific settings, particularly in normative rather than purely formal contexts. Although...

arXiv:2603.06424v1 Announce Type: new Abstract: Large language models (LLMs) have recently reshaped Automated Essay Scoring (AES), yet prior studies typically examine individual techniques in isolation, limiting understanding of their relative merits for English as a Second Language (L2) writing. To...

arXiv:2603.06428v1 Announce Type: new Abstract: Research in AI using Large-Language Models (LLMs) is rapidly evolving, and the comparison of their performance with human reasoning has become a key concern. Prior studies have indicated that LLMs and humans share similar biases,...

arXiv:2603.06503v1 Announce Type: new Abstract: Recent advances in multimodal Retrieval-Augmented Generation (RAG) enable Large Language Models (LLMs) to analyze enterprise spreadsheet workbooks containing millions of cells, cross-sheet dependencies, and embedded visual artifacts. However, state-of-the-art approaches exclude critical context through single-pass...

arXiv:2603.05556v1 Announce Type: new Abstract: Integer sequences in the OEIS span values from single-digit constants to astronomical factorials and exponentials, making prediction challenging for standard tokenised models that cannot handle out-of-vocabulary values or exploit periodic arithmetic structure. We present IntSeqBERT,...

arXiv:2603.05806v1 Announce Type: new Abstract: Mixture of Experts (MoE) models enable parameter-efficient scaling through sparse expert activations, yet optimizing their inference and memory costs remains challenging due to limited understanding of their specialization behavior. We present a systematic analysis of...

arXiv:2603.06248v1 Announce Type: new Abstract: Understanding the intricate non-convex training dynamics of softmax-based models is crucial for explaining the empirical success of transformers. In this article, we analyze the gradient flow dynamics of the value-softmax model, defined as ${L}(\mathbf{V} \sigma(\mathbf{a}))$,...

Abstract In this commentary, we discuss the nature of reversible and irreversible questions, that is, questions that may enable one to identify the nature of the source of their answers. We then introduce GPT-3, a third-generation, autoregressive language model that...

The surge in legal text production has amplified the workload for legal professionals, making many tasks repetitive and time-consuming. Furthermore, the complexity and specialized language of legal documents pose challenges not just for those in the legal domain but also...

Vanderbilt University is a globally renowned center for scholarly research, informed and creative teaching, and service to the community and society at large. The Vanderbilt community is committed to the highest academic standards, a spirit of intellectual freedom and a...

Almost all law is expressed in natural language; therefore, natural language processing (NLP) is a key component of understanding and predicting law. Natural language processing converts unstructured text into a formal representation that computers can understand and analyze. This technology...

By REBEKAH NINAN. Full Text. lawreview - Minnesota Law Review

AbstractGovernance efforts for artificial intelligence (AI) are taking on increasingly more concrete forms, drawing on a variety of approaches and instruments from hard regulation to standardisation efforts, aimed at mitigating challenges from high-risk AI systems. To implement these and other...

Abstract Artificial Intelligence (‘AI’) technologies present great opportunities for the investment management industry (as well as broader financial services). However, there are presently no regulations specifically aiming at AI in investment management. Does this mean that AI is currently unregulated?...

Exceptional Cases By Emily Cauble Full essay here. Machine Gun Funk: The Unusual Analysis of "Dangerous and Unusual" By Gregory S. Parks & Vivian Bolen Full essay here. Nipping it in the Bud: The Promise and Perils of Tort Litigation...

Articles, Essays, & Tributes Notes Headnotes Volume 110: Fall Issue Volume 108: Symposium Supplement De Novo Blog Tweets by MinnesotaLawRev barne102 - Minnesota Law Review

The profession of law has changed along with many other industries due to the quick development of artificial intelligence (AI). However, in applications specialized to the legal domain, general-purpose AI models like ChatGPT, DeepSeek, and Gemini show limits. This evaluation...

Abstract Two critical policy questions will determine the impact of generative artificial intelligence (AI) on the knowledge economy and the creative sector. The first concerns how we think about the training of such models—in particular, whether the creators or owners...

This paper examines the evolution of legal personhood and explores whether historical precedents—from corporate personhood to environmental legal recognition—can inform frameworks for governing artificial intelligence (AI). By tracing the development of persona ficta in Roman law and subsequent expansions of...

Abstract This study investigates the extent to which the conclusion of a decision can be predicted from other parts of the decision from quasi-judicial institutions using machine learning. Predicting conclusions in quasi-judicial bodies poses unique challenges and opportunities because the...