China

Judicial work depends on close reading of long
records, charge sheets, pleadings, annexures,
orders, often spanning hundreds of pages. With
limited staff support, exhaustive reading during
hearings is impractical. We present CourtNav,
a voice-guided, anchor-first navigator for legal
PDFs that maps a judge’s spoken command
(e.g., “go to paragraph 23”, “highlight the contradiction in the cross-examination”) directly
to a highlighted paragraph in seconds. CourtNav transcribes the command, classifies intent
with a grammar-first, LLM-backed router, retrieves over a layout-aware hybrid index, and
auto-scrolls the viewer to the cited span while
highlighting it and close alternates. By design, the interface shows only grounded pas-
sages, never free text, keeping evidence verifiable and auditable. This need is acute in India, where judgments and cross-examinations
notoriously long.In a pilot on representative charge sheets, pleadings, and orders, median time-to-relevance drops from 3–5 minutes (manual navigation) to 10–15 seconds;
with quick visual verification included, 30–45
seconds. Under fixed time budgets, this
navigation-first design increases the breadth of
the record actually consulted while preserving
control and transparency

EMNLP 2025

CourtNav: Voice-Guided, Anchor-Accurate Navigation of Long Legal Documents in Courtrooms

a retrieval-augmented generation.

document qa

information retrieval

workshop paper

## Welcome!
"I am excited to welcome you to this year’s edition of the Conference on Empirical Methods in Natural Language Processing! Importantly, it marks the 30th edition of EMNLP. With over 8,000 submissions, more than 3,000 accepted papers, and thousands of attendees, we have come a long way from that first
workshop, which had 14 accepted papers. As the field looks ahead, Suzhou is the fitting location for celebrating this milestone: rooted in a long literary tradition, yet modern and forward-looking, and home to a large share of the NLP community."<br>

*Message from the General Chair, Dirk Hovy*





<br>

Celebrate 30 Years of EMNLP! 
EMNLP 2025 will be held in Suzhou, China from November 5th to November 9th, 2025.

This position paper argues that European copyright law has struggled to keep pace with the development of large language models (LLMs), possibly creating a fundamental epistemic misalignment: copyright compliance relies on qualitative, context-dependent standards, while LLM development is governed by quantitative, proactive metrics. This gap means that technical safeguards, by themselves, may be insufficient to reliably demonstrate legal compliance. We identify several practical limitations in the existing EU legal frameworks, including ambiguous "lawful access" rules, fragmented opt-outs, and vague disclosure duties. We then discuss technical measures such as provenance-first data governance, machine unlearning for post-hoc removal, and synthetic data generation, showing their promise but also their limits.
Finally, we propose a path forward grounded in legal-technical co-design, suggesting directions for standardising machine-readable opt-outs, disclosure templates, clarifying core legal terms, and developing legally-informed benchmarks and evidence standards. We conclude that such an integrated framework is essential to make compliance auditable, thus protected creators' rights while enabling responsible AI innovation at scale.

Copyright Infringement by Large Language Models in the EU: Misalignment, Safeguards, and the Path Forward

One of the first steps in the judicial process
is finding the applicable statutes/laws based
on the facts of the current situation. Manu-
ally searching through multiple legislation and
laws to find the relevant statutes can be time-
consuming, making the Legal Statute Identi-
fication (LSI) task important for reducing the
workload, helping improve the efficiency of
the judicial system. To address this gap, we
present a novel knowledge graph-enhanced ap-
proach for Legal Statute Identification (LSI) in
Indian legal documents using Large Language
Models, incorporating structural relationships
from the Indian Penal Code (IPC) the main leg-
islation codifying criminal laws in India. On
the IL-TUR benchmark, explicit KG inference
significantly enhances recall without sacrific-
ing competitive precision. Augmenting LLM
prompts with KG context, though, merely en-
hances coverage at the expense of precision,
underscoring the importance of good rerank-
ing techniques. This research provides the first
complete IPC knowledge graph and shows that
organized legal relations richly augment statute
retrieval, subject to being integrated into lan-
guage models in a judicious way. Our code and
data are publicly available at Github. (https://github.com/SiddharthShukla48/NyayGraph)

NyayGraph: A Knowledge Graph Enhanced Approach for Legal Statute Identification in Indian Law using Large Language Models

The rise of influencer marketing has blurred boundaries between organic content and sponsored content, making the enforcement of legal rules relating to transparency challenging. Effective regulation requires applying legal knowledge with a clear purpose and reason, yet current detection methods of undisclosed sponsored content generally lack legal grounding or operate as opaque ``black boxes.'' Using 1,143 Instagram posts, we compare \textit{gpt-5-nano} and \textit{gemini-2.5-flash-lite} under three prompting strategies with controlled levels of legal knowledge provided. Both models perform strongly in classifying content as sponsored or not (F1 up to 0.93), with Gemini favouring recall (0.93) and GPT favouring precision (0.95), though performance drops by over 10 points on ambiguous cases. We further develop a taxonomy of reasoning errors, showing frequent citation omissions (28.57%), unclear references (20.71%), and hidden ads exhibiting the highest miscue rate (28.57%). While adding regulatory text to the prompt improves explanation quality, it does not consistently improve detection accuracy. The contribution of this paper is threefold. First, it makes a novel addition to regulatory compliance technology by providing a taxonomy of common errors in LLM-generated legal reasoning to evaluate whether automated moderation is not only accurate but also legally robust, thereby advancing the transparent detection of influencer marketing content. Second, it features an original dataset of LLM explanations annotated by two students who were trained in influencer marketing law. Third, it combines quantitative and qualitative evaluation strategies for LLM explanations and critically reflects on how these findings can support advertising regulatory bodies in automating moderation processes on a solid legal foundation.

Evaluating LLM-Generated Legal Explanations for Regulatory Compliance in Social Media Influencer Marketing

As social systems become more complex, legal articles have grown increasingly intricate, making it harder for humans to identify potential conflicts among them, particularly when drafting new laws or applying existing ones. Despite its importance, no method has been proposed to detect such conflicts. We introduce a new legal NLP task, Legal Article Conflict Detection (LACD), which aims to identify conflicting articles within a given body of law. To address this task, we propose GReX, a novel graph neural network-based retrieval method. Experimental results show that GReX significantly outperforms existing methods, achieving improvements of 44.8% in nDCG@50, 32.8% in Recall@50, and 39.8% in Retrieval F1@50. Our codes are in github.com/asmath472/LACD-public.

GReX: A Graph Neural Network-Based Rerank-then-Expand Method for Detecting Conflicts Among Legal Articles in Korean Criminal Law

Legal Passage Retrieval (LPR) systems are crucial as they help practitioners save time when drafting legal arguments. However, it remains an underexplored avenue. One primary reason is the significant vocabulary mismatch between the query and the target passage. To address this, we propose a simple yet effective method, the $\textbf{G}$enerative q$\textbf{u}$ery $\textbf{RE}$writer $\textbf{(GuRE)}$. We leverage the generative capabilities of Large Language Models (LLMs) by training the LLM for query rewriting. $\textit{"Rewritten queries"}$ help retrievers to retrieve target passages by mitigating vocabulary mismatch. Experimental results show that GuRE significantly improves performance in a retriever-agnostic manner, outperforming all baseline methods. Further analysis reveals that different training objectives lead to distinct retrieval behaviors, making GuRE more suitable than direct retriever fine-tuning for real-world applications. Codes are avaiable at github.com/daehuikim/GuRE.

GuRE:Generative Query REwriter for Legal Passage Retrieval

We introduce GreekBarBench, a benchmark that evaluates LLMs on legal questions across five different legal areas from the Greek Bar exams, requiring citations to statutory articles and case facts. To tackle the challenges of free-text evaluation, we propose a three-dimensional scoring system combined with an LLM-as-a-judge approach. We also develop a meta-evaluation benchmark to assess the correlation between LLM-judges and human expert evaluations, revealing that simple, span-based rubrics improve their alignment. Our systematic evaluation of 13 proprietary and open-weight LLMs shows that even though the best models outperform average expert scores, they fall short of the 95th percentile of experts.

GreekBarBench: A Challenging Benchmark for Free-Text Legal Reasoning and Citations

This paper investigates personal perceptions of mental workload through an innovative, non-directive corpus annotation method, allowing individuals of diverse profiles to define their own dimensions of annotation based on their personal perception. It contrasts with traditional approaches guided by explicit objectives and strict guidelines.
Mental workload, a multifaceted concept in psychology, is characterized through various academic definitions and models. Our research, aligned with the principles of the perspectivist approach, aims to examine the degree to which individuals share a common understanding of this concept when reading the same texts. It seeks to compare the corpus produced by this non-directive annotation method. 
The participants, mainly employees of a large French enterprise and some academic experts on mental workload, were given the freedom to propose labels and annotate a set of texts.
The experimental protocol revealed notable similarities in labels, segments, and overall annotation behavior, despite the absence of predefined guidelines. These findings suggest that individuals, given the freedom, tend to develop overlapping representations of mental workload. Furthermore, they demonstrate how non-directive annotation can uncover shared and diverse perceptions of complex concepts like mental workload, contributing to a richer understanding of how such perceptions are constructed across different individuals.

Non-directive corpus annotation to reveal individual perspectives with underspecified guidelines: the case of mental workload

Multi-party dialogue is a critical setting for studying collaborative reasoning and decision-making, yet existing datasets rarely focus on structured, in-depth complex reasoning tasks. We introduce DeliChess, a novel dataset of group deliberation dialogues in which participants collaboratively solve multiple-choice chess puzzles. Each group first completes the puzzle individually, then engages in a multi-party discussion before submitting a revised collective answer. The dataset includes 107 dialogues with full transcripts, pre- and post-discussion choices, and metadata on puzzle difficulty and move quality. We evaluate performance using three metrics based on chess engine evaluations, and find that deliberation significantly improves group accuracy. We further analyse the role of probing utterances (i.e., messages that elicit proposals, justifications, or strategic reflection) using a classifier trained on prior deliberation data. While probing makes group performance more variable after discussion, it does not consistently lead to better performance. Our dataset offers a rich testbed for modelling group reasoning, dialogue dynamics, and the resolution of differing perspectives and opinions in a well-defined strategic domain.

DeliChess: A Multi-party Dialogue Dataset for Deliberation in Chess Problem Solving


Identifying subjective phenomena, such as irony in language, poses unique challenges, as these tasks involve subjective interpretation shaped by both cultural and individual perspectives. Unlike conventional models that rely on aggregated annotations, perspectivist approaches aim to capture the diversity of viewpoints by leveraging the knowledge of specific annotator groups, promoting fairness and representativeness. However, such models often incur substantial computational costs, particularly when fine-tuning large-scale pre-trained language models. We also observe that the fine-tuning process can negatively impact fairness, producing certain perspective models that are underrepresented and have limited influence on the outcome. To address these, we explore two complementary strategies: (i) the adoption of traditional machine learning algorithms—such as Support Vector Machines, Random Forests, and XGBoost—as lightweight alternatives; and (ii) the application of calibration techniques to reduce imbalances in inference generation across perspectives. Our results demonstrate up to 12× faster processing with no statistically significant drop in accuracy. Notably, calibration significantly enhances fairness, reducing inter-group bias and leading to more balanced predictions across diverse social perspectives.

Calibration as a Proxy for Fairness and Efficiency in a Perspectivist Ensemble Approach to Irony Detection

The task of perspective-aware classification introduces a bottleneck in terms of parametric efficiency that did not get enough recognition in existing studies. In this article, we aim to address this issue by applying an existing architecture, the hypernetwork+adapters combination, to perspectivist classification. Ultimately, we arrive at a solution that can compete with specialized models in adopting user perspectives on hate speech and toxicity detection, while also making use of considerably fewer parameters. Our solution is architecture-agnostic and can be applied to a wide range of base models out of the box.

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