China

Large Language Models (LLMs) are increasingly used to generate user-tailored summaries, adapting outputs to specific stakeholders. In legal contexts, this raises important questions about motivated reasoning --- how models strategically frame information to align with a stakeholder&#39;s position within the legal system. Building on theories of legal realism and recent trends in legal practice, we investigate how LLMs respond to prompts conditioned on different legal roles (e.g., judges, prosecutors, attorneys) when summarizing judicial decisions. We introduce an evaluation framework grounded in legal fact and reasoning inclusion, also considering favorability towards stakeholders. Our results show that even when prompts include balancing instructions, models exhibit selective inclusion patterns that reflect role-consistent perspectives. These findings raise broader concerns about how similar alignment may emerge as LLMs begin to infer user roles from prior interactions or context, even without explicit role instructions. Our results underscore the need for role-aware evaluation of LLM summarization behavior in high-stakes legal settings.

EMNLP 2025

Modeling Motivated Reasoning in Law: Evaluating Strategic Role Conditioning in LLM Summarization

Large Language Models (LLMs) are increasingly used to generate user-tailored summaries, adapting outputs to specific stakeholders. In legal contexts, this raises important questions about motivated reasoning --- how models strategically frame information to align with a stakeholder's position within the legal system. Building on theories of legal realism and recent trends in legal practice, we investigate how LLMs respond to prompts conditioned on different legal roles (e.g., judges, prosecutors, attorneys) when summarizing judicial decisions. We introduce an evaluation framework grounded in legal fact and reasoning inclusion, also considering favorability towards stakeholders. Our results show that even when prompts include balancing instructions, models exhibit selective inclusion patterns that reflect role-consistent perspectives. These findings raise broader concerns about how similar alignment may emerge as LLMs begin to infer user roles from prior interactions or context, even without explicit role instructions. Our results underscore the need for role-aware evaluation of LLM summarization behavior in high-stakes legal settings.

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*

[**Link to Conference Handbook**](https://drive.google.com/file/d/1johU5QqVVYO4RfH7QcIORr7qrVBdzdwC/view?usp=sharing)





<br>

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

Text summarization systems face significant adaptation costs when deployed across diverse domains, requiring expensive few-shot learning or manual prompt engineering. 
We propose a cost-effective domain adaptation framework that generates reusable summarization guidelines using only two reference summaries and three LLM inferences. 
Our approach works by having the model compare its own generated summaries against domain specific reference summaries in a one time preparation step that derives concise natural language guidelines that capture the summarization patterns of the target domain. 
These guidelines are then appended to the summarization prompt to adapt the LLM to the target domain at a minimal cost. 
We evaluate our method across diverse model sizes on three distinct summarization domains: Lawsuits, ArXiv papers, and Patents. 
Automatic metrics show that guideline-based adaptation achieves comparable or superior performance compared to in-context learning and zero-shot baselines. 
An LLM preference evaluation using the latest models shows that summaries generated using such guidelines are superior to the zero-shot or in-context learning summarization prompts. 
Our method enables efficient domain adaptation of text summarizer LLMs with a minimal resource overhead, making specialized summarization particularly accessible for agentic systems that require to process heterogeneous texts in enterprise environments.

Domain Adapted Text Summarization with Self-Generated Guidelines

Legal Argument Mining (LAM) is a complex challenge for humans and language models alike. This paper explores the application of Large Language Models (LLMs) in LAM, focusing on the identification of fine-grained argument types within judgment texts. We compare the performance of Flan-T5 and Llama 3 models against a baseline RoBERTa model to study if the advantages of magnitude-bigger LLMs can be leveraged for this task. Our study investigates the effectiveness of fine-tuning and prompting strategies in enhancing the models’ ability to discern nuanced argument types. Despite employing state-of-the-art techniques, our findings indicate that neither fine-tuning nor prompting could surpass the performance of a domain-pre-trained encoder-only model. This highlights the challenges and limitations in adapting general-purpose large language models to the specialized domain of legal argumentation. The insights gained from this research contribute to the ongoing discourse on optimizing NLP models for complex, domain-specific tasks. Our code and data for reproducibility are available at https://github.com/trusthlt/legal-argument-spans.

Contemporary LLMs struggle with extracting formal legal arguments

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

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

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.

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