GenAI4ELab/papercli-papers huggingface dataset:
Towards Reliable Retrieval in RAG Systems for Large Legal Datasets
Authors: Markus Reuter; Tobias Lingenberg; Ruta Liepina; Francesca Lagioia; Marco Lippi; Giovanni Sartor; Andrea Passerini; Burcu Sayin
Venue: EMNLP 2025 (Proceedings of the Natural Legal Language Processing Workshop 2025)

Abstract: Retrieval-Augmented Generation (RAG) is a promising approach to mitigate hallucinations in Large Language Models (LLMs) for legal applications, but its reliability is critically dependent on the accuracy of the retrieval step. This is particularly challenging in the legal domain, where large databases of structurally similar documents often cause retrieval systems to fail. In this paper, we address this challenge by first identifying and quantifying a critical failure mode we term Document-Level Retrieval Mismatch (DRM), where the retriever selects information from entirely incorrect source documents. To mitigate DRM, we investigate a simple and computationally efficient technique which we refer to as Summary-Augmented Chunking (SAC). This method enhances each text chunk with a document-level synthetic summary, thereby injecting crucial global context that would otherwise be lost during a standard chunking process. Our experiments on a diverse set of legal information retrieval tasks show that SAC greatly reduces DRM and, consequently, also improves text-level retrieval precision and recall. Interestingly, we find that a generic summarization strategy outperforms an approach that incorporates legal expert domain knowledge to target specific legal elements. Our work provides evidence that this practical, scalable, and easily integrable technique enhances the reliability of RAG systems when applied to large-scale legal document datasets.
Translating Tax Law to Code with LLMs: A Benchmark and Evaluation Framework
Authors: Gabriele Lorenzo; Aldo Pietromatera; Nils Holzenberger
Venue: EMNLP 2025 (Proceedings of the Natural Legal Language Processing Workshop 2025)

Abstract: Catala is a domain-specific programming language for tax law, meant to facilitate the translation of legal text into executable computer code, thanks to a syntax close to that of legal language and reasoning. Legal statutes paired with their Catala translation have been published online periodically, but manual translation remains labor-intensive. In this work, we develop a benchmark for the evaluation of Catala code generation from legal text, including a training set to fine-tune Large Language Models. To assess the quality of the generated code, we introduce an evaluation framework extending current metrics for code generation. Our experiments with few-shot learning, as well as fine-tuned models, suggest the feasibility of automating legal code generation, and contrast with prior attempts to translate legal language into a formal representation.
Beyond the Haystack: Sensitivity to Context in Legal Reference Recall
Authors: Eric Xia; Karthik Srikumar; Keshav Karthik; Advaith Renjith; Ashwinee Panda
Venue: EMNLP 2025 (Proceedings of the Natural Legal Language Processing Workshop 2025)

Abstract: Reference retrieval is critical for many applications in the legal domain, for instance in determining which case texts support a particular claim. However, existing benchmarking methods do not rigorously enable evaluation of recall capabilities in previously unseen contexts. We develop an evaluation framework from U.S. court opinions which ensures models have no prior knowledge of case results or context. Applying our framework, we identify an consistent gap across models and tasks between traditional needle-in-a-haystack retrieval and actual performance in legal recall. Our work shows that standard needle-in-a-haystack benchmarks consistently overestimate recall performance in the legal domain. By isolating the causes of performance degradation to contextual informativity rather than distributional differences, our findings highlight the need for specialized testing in reference-critical applications, and establish an evaluation framework for improving retrieval across informativity levels.
Machine Unlearning of Personally Identifiable Information in Large Language Models
Authors: Dan Parii; Thomas van Osch; Chang Sun
Venue: EMNLP 2025 (Proceedings of the Natural Legal Language Processing Workshop 2025)

Abstract: Pretrained LLMs are trained on massive web-scale datasets, which often contain personally identifiable information (PII), raising serious legal and ethical concerns. A key research challenge is how to effectively unlearn PII without degrading the model’s utility or leaving implicit knowledge that can be exploited.This study proposes UnlearnPII, a benchmark designed to evaluate the effectiveness of PII unlearning methods, addressing limitations in existing metrics that overlook implicit knowledge and assess all tokens equally. Our benchmark focuses on detecting PII leakage, testing model robustness through obfuscated prompts and jailbreak attacks over different domains, while measuring utility and retention quality.To advance practical solutions, we propose a new PII unlearning method - PERMUtok. By applying token-level noise, we achieve 1) simplified integration into existing workflows, 2) improved retention and output quality, while maintaining unlearning effectiveness. The code is open-source and publicly available.
Modeling Motivated Reasoning in Law: Evaluating Strategic Role Conditioning in LLM Summarization
Authors: Eunjung Cho; Alexander Hoyle; Yoan Hermstrüwer
Venue: EMNLP 2025 (Proceedings of the Natural Legal Language Processing Workshop 2025)

Abstract: 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.
Label-Free Distinctiveness: Building a Continuous Trademark Scale via Synthetic Anchors
Authors: Huihui Xu; Kevin D. Ashley
Venue: EMNLP 2025 (Proceedings of the Natural Legal Language Processing Workshop 2025)

Abstract: Trademark law protects distinctive marks that are able to identify and distinguish goods or services. The Abercrombie spectrum classifies marks from generic to fanciful based on distinctiveness. The Abercrombie spectrum employs hard buckets while the real world ofbranding rarely falls into neat bins: marks often hover at the blurry border between “descriptive” and “suggestive” for example. Byrequiring trademark examiners or researchers to pick one of the five buckets, one loses useful information where the lines get blurry. Sohard boundaries obscure valuable gradations of meaning. In this work, we explore creating a continuous ruler of distinctiveness asa complementary diagnostic tool to the original buckets. The result is a label-free ladder, where every mark, real or synthetic, gets a real-valued score. These continuous scores reveal subtle distinctions among marks and provide interpretable visualizations that help practitioners understand where a mark falls relative to established anchors. Testing with 95 expert-classified trademark examples achieves a Spearman’s ρ = 0.718 and Pearson’s r = 0.724 against human labels, while offering intuitive visualizations on the continuous spectrum. Ademo can be found at https://distinctiveness-ruler-demo.streamlit.app/.
Copyright Infringement by Large Language Models in the EU: Misalignment, Safeguards, and the Path Forward
Authors: Noah Scharrenberg; Chang Sun
Venue: EMNLP 2025 (Proceedings of the Natural Legal Language Processing Workshop 2025)

Abstract: 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.
Grounded Answers from Multi-Passage Regulations: Learning-to-Rank for Regulatory RAG
Authors: Tuba Gokhan; Ted Briscoe
Venue: EMNLP 2025 (Proceedings of the Natural Legal Language Processing Workshop 2025)

Abstract: Regulatory compliance questions often require aggregating evidence from multiple, interrelated sections of long, complex documents. To support question-answering (QA) in this setting, we introduce ObliQA-MP, a dataset for multi-passage regulatory QA, extending the earlier ObliQA benchmark (CITATION), and improve evidence quality with an LLM–based validation step that filters out ~20% of passages missed by prior natural language inference (NLI) based filtering. Our benchmarks show a notable performance drop from single- to multi-passage retrieval, underscoring the challenges of semantic overlap and structural complexity in regulatory texts. To address this, we propose a feature-based learning-to-rank (LTR) framework that integrates lexical, semantic, and graph-derived information, achieving consistent gains over dense and hybrid baselines. We further add a lightweight score-based filter to trim noisy tails and an obligation-centric prompting technique. On ObliQA-MP, LTR improves retrieval (Recall@10/MAP@10/nDCG@10) over dense, hybrid, and fusion baselines. Our generation approach, based on domain-specific filtering plus prompting, achieves strong scores using the RePAS metric (CITATION) on ObliQA-MP, producing faithful, citation-grounded answers. Together, ObliQA-MP and our validation and RAG systems offer a stronger benchmark and a practical recipe for grounded, citation-controlled QA in regulatory domains.
NyayGraph: A Knowledge Graph Enhanced Approach for Legal Statute Identification in Indian Law using Large Language Models
Authors: Siddharth Shukla; Tanuj Tyagi; Abhay Singh Bisht; Ashish Sharma; Basant Agarwal
Venue: EMNLP 2025 (Proceedings of the Natural Legal Language Processing Workshop 2025)

Abstract: One of the first steps in the judicial processis finding the applicable statutes/laws basedon the facts of the current situation. Manu-ally searching through multiple legislation andlaws to find the relevant statutes can be time-consuming, making the Legal Statute Identi-fication (LSI) task important for reducing theworkload, helping improve the efficiency ofthe judicial system. To address this gap, wepresent a novel knowledge graph-enhanced ap-proach for Legal Statute Identification (LSI) inIndian legal documents using Large LanguageModels, incorporating structural relationshipsfrom the Indian Penal Code (IPC) the main leg-islation codifying criminal laws in India. Onthe IL-TUR benchmark, explicit KG inferencesignificantly enhances recall without sacrific-ing competitive precision. Augmenting LLMprompts 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 firstcomplete IPC knowledge graph and shows thatorganized legal relations richly augment statuteretrieval, subject to being integrated into lan-guage models in a judicious way. Our code anddata are publicly available at Github. (https://github.com/SiddharthShukla48/NyayGraph)
Evaluating LLM-Generated Legal Explanations for Regulatory Compliance in Social Media Influencer Marketing
Authors: Haoyang Gui; Thales Bertaglia; Taylor Annabell; Catalina Goanta; Tjomme Dooper; Gerasimos Spanakis
Venue: EMNLP 2025 (Proceedings of the Natural Legal Language Processing Workshop 2025)

Abstract: 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 gpt-5-nano and 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.
Validate Your Authority: Benchmarking LLMs on Multi-Label Precedent Treatment Classification
Authors: M. Mikail Demir; M Abdullah Canbaz
Venue: EMNLP 2025 (Proceedings of the Natural Legal Language Processing Workshop 2025)

Abstract: Automating the classification of negative treatment in legal precedent is a critical yet nuanced NLP task where misclassification carries significant risk. To address the shortcomings of standard accuracy, this paper introduces a more robust evaluation framework. We benchmark modern Large Language Models on a new, expert-annotated dataset of 239 real-world legal citations and propose a novel Average Severity Error metric to better measure the practical impact of classification errors. Our experiments reveal a performance split: Google’s Gemini 2.5 Flash achieved the highest accuracy on a high-level classification task (79.1%), while OpenAI’s GPT-5-mini was the top performer on the more complex fine-grained schema (67.7%). This work establishes a crucial baseline, provides a new context-rich dataset, and introduces an evaluation metric tailored to the demands of this complex legal reasoning task.
Labor Lex: A New Portuguese Corpus and Pipeline for Information Extraction in Brazilian Legal Texts
Authors: Pedro Vitor Quinta de Castro; Nádia Félix Felipe Da Silva
Venue: EMNLP 2025 (Proceedings of the Natural Legal Language Processing Workshop 2025)

Abstract: Relation Extraction (RE) is a challenging Natural Language Processing task that involves identifying named entities from text and classifying the relationships between them. When applied to a specific domain, the task acquires a new layer of complexity, handling the lexicon and context particular to the domain in question. In this work, this task is applied to the Legal domain, specifically targeting Brazilian Labor Law. Architectures based on Deep Learning, with word representations derived from Transformer Language Models (LM), have shown state-of-the-art performance for the RE task. Recent works on this task handle Named Entity Recognition (NER) and RE either as a single joint model or as a pipelined approach. In this work, we introduce Labor Lex, a newly constructed corpus based on public documents from Brazilian Labor Courts. We also present a pipeline of models trained on it. Different experiments are conducted for each task, comparing supervised training using LMs and In-Context Learning (ICL) with Large Language Models (LLM), and verifying and analyzing the results for each one. For the NER task, the best achieved result was 89.97% F1-Score, and for the RE task, the best result was 82.38% F1-Score. The best results for both tasks were obtained using the supervised training approach.
Evaluating the Role of Verifiers in Test-Time Scaling for Legal Reasoning Tasks
Authors: Davide Romano; Jonathan Richard Schwarz; Daniele Giofrè
Venue: EMNLP 2025 (Proceedings of the Natural Legal Language Processing Workshop 2025)

Abstract: Test-time scaling (TTS) techniques can improve the performance of large language models (LLMs) at the expense of additional computation and latency. While TTS has proven effective in formal domains such as mathematics and programming (Snell et al., 2024; Chen et al., 2024), its value in argumentative domains such as law remains underexplored. We present an empirical study of verifier-based TTS methods for legal multiple-choice QA (MCQA) across five benchmarks. Using a family of 7 reward models, we evaluate both outcome-level (Best-of-N) and process-level (tree search) verification under realistic low-N budgets. Our analysis systematically investigates how verifier utility is affected by key properties such as domain specialization, model size, and supervision type (process-supervised PRMs vs. outcome-only ORMs), even when applied across different roles.
Domain Adapted Text Summarization with Self-Generated Guidelines
Authors: Andrianos Michail; Bartosz Rudnikowicz; Pavlos Fragkogiannis; Cristina Kadar
Venue: EMNLP 2025 (Proceedings of the Natural Legal Language Processing Workshop 2025)

Abstract: 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.
PILOT-Bench: A Benchmark for Legal Reasoning in the Patent Domain with IRAC-Aligned Classification Tasks
Authors: Yehoon Jang; Chaewon Lee; Hyun-seok Min; Sungchul Choi
Venue: EMNLP 2025 (Proceedings of the Natural Legal Language Processing Workshop 2025)

Abstract: The Patent Trial and Appeal Board (PTAB) of the USPTO adjudicates thousands of *ex parte* appeals each year, requiring the integration of technical understanding and legal reasoning. While large language models (LLMs) are increasingly applied in patent and legal practice, their use has remained limited to lightweight tasks, with no established means of systematically evaluating their capacity for structured legal reasoning in the patent domain. To address this gap, we introduce **PILOT-Bench** (**P**atent **I**nva**L**idati**O**n **T**rial Benchmark), a dataset and benchmark that aligns PTAB decisions with USPTO patent data at the case level and formalizes three IRAC-aligned classification tasks: Issue Type, Board Authorities, and Subdecision. We evaluate a diverse set of commercial and open-source LLMs and conduct analyses across multiple perspectives, including input-variation settings, model families, and error tendencies. Notably, on the Issue Type task, commercial models consistently exceed 0.75 in Exact Match, whereas the strongest open-source model (Qwen-8B) achieves performance around 0.56, highlighting the substantial gap in reasoning capabilities. PILOT-Bench establishes a foundation for the systematic evaluation of patent-domain legal reasoning and points toward future directions for improving LLMs through dataset design and model alignment. All data, code, and benchmark resources are available at https://github.com/TeamLab/pilot-bench.
Efficient Prompt Optimisation for Legal Text Classification with Proxy Prompt Evaluator
Authors: Hyunji Lee; Kevin Chenhao Li; Matthias Grabmair; Shanshan Xu
Venue: EMNLP 2025 (Proceedings of the Natural Legal Language Processing Workshop 2025)

Abstract: Prompt optimization aims to systematically refine prompts to enhance a language model’s performance on specific tasks. Fairness detection in Terms of Service (ToS) clauses is a challenging legal NLP task that demands carefully crafted prompts to ensure reliable results. However, existing prompt optimization methods are often computationally expensive due to inefficient search strategies and costly prompt candidate scoring. In this paper, we propose a framework that combines Monte Carlo Tree Search (MCTS) with a proxy prompt evaluator to more effectively explore the prompt space while reducing evaluation costs. Experiments demonstrate that our approach achieves higher classification accuracy and efficiency than baseline methods under a constrained computation budget.