China

Large language models (LLMs) such as GPT-4, Claude and LLaMA are routinely used to evaluate long-form text generated by language models. We study the ability of these models to identify low quality texts, an increasingly rare subset of output which is of great interest to pinpoint during development. We present experiments with a panel of LLM judges, and crowd-sourced approximations of reference judgments. Pinpointing sub-par outputs is a difficult task for both models and crowdworkers, with models doing overall better. Moreover, unlike findings in prior work on factoid question answering, panels of cheaper models do not agree as well with high quality developer judgments of low quality as panels of frontier models. We present qualitative and quantitative analysis of the relative strengths of models in the panel, gleaning insights why they yield better results over a single model.

EMNLP 2025

LLM-as-a-Judge Failures at Automating the Identification of Poor Quality Outputs in Free-Form Texts

crowd source evaluation

long text evaluation

llm-as-a-judge

model analysis

## 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.

workshop paper

Language Models (LMs) have revolutionized natural language processing, enabling high-quality text generation through prompting and in-context learning. However, models often struggle with long-context summarization due to positional biases, leading to suboptimal extraction of critical information. There are techniques to improve this with fine-tuning, pipelining, or using complex techniques, which have their own challenges. To solve these challenges, we propose QA-prompting - a simple prompting method for summarization that utilizes question-answering as an intermediate step prior to summary generation. Our method extracts key information and enriches the context of text to mitigate positional biases and improve summarization in a single LM call per task without requiring fine-tuning or pipelining. Experiments on multiple datasets belonging to different domains using ten state-of-the-art pre-trained models demonstrate that QA-prompting outperforms baseline and other state-of-the-art methods, achieving up to 29% improvement in ROUGE scores. This provides an effective and scalable solution for summarization and highlights the importance of domain-specific question selection for optimal performance.

QA-prompting: Improving Summarization with Large Language Models using Question-Answering

The potential of large language models (LLMs) in contract legal risk analysis remains underexplored. In response, this paper introduces ContractEval, the first benchmark to thoroughly evaluate whether open-source LLMs could match proprietary LLMs in identifying clause-level legal risks in commercial contracts. Using the Contract Understanding Atticus Dataset (CUAD), we assess 4 proprietary and 15 open-source LLMs. Our results highlight five key findings: (1) Proprietary models outperform open-source models in both correctness and output effectiveness. (2) Larger open-source models generally perform better, though the improvement slows down as models get bigger. (3) Reasoning ("thinking”) mode improves output effectiveness but reduces correctness, likely due to over-complicating simpler tasks. (4) Open-source models generate “no related clause” responses more frequently even when relevant clauses are present. (5) Model quantization speed up inference but at the cost of performance drop, showing the tradeoff between efficiency and accuracy. These findings suggest that while most LLMs perform at a level comparable to junior legal assistants, open-source models require targeted fine-tuning to ensure correctness and effectiveness in high-stakes legal settings. ContractEval offers a solid benchmark to guide future development of legal-domain LLMs.

ContractEval: Benchmarking LLMs for Clause-Level Legal Risk Identification in Commercial Contracts

Wills must comply with jurisdiction-specific statutory provisions to be valid, but retrieving the relevant laws for execution, validation, and probate remains labor-intensive and error-prone. Prior legal information retrieval (LIR) research has addressed contracts, criminal law, and judicial decisions, but wills and probate law remain largely unexplored, with no prior work on retrieving statutes for will validity assessment. We propose a legal information retrieval framework that combines lexical and semantic retrieval in a hybrid pipeline with large language model (LLM) reasoning to retrieve the most relevant provisions for a will statement. Evaluations on annotated will-statement datasets from the U.S. states of Tennessee and Idaho using six LLMs show that our hybrid framework consistently outperforms zero-shot baselines. Notably, when paired with our hybrid retrieval pipeline, GPT-5-mini achieves the largest relative accuracy gains, improving by 41.09 points on the Tennessee and 48.68 points on the Idaho test set. We observed similarly strong improvements across all models and datasets.

A Framework to Retrieve Relevant Laws for Will Execution

The Retrieval-Augmented Generation (RAG) systems' performance on Thai legal question answering is still limited, especially for questions requiring extensive, complex legal reasoning. To address these limitations, we introduce a resource-efficient approach that aligns Large Language Models (LLMs) for improved citation accuracy and response quality using Group-Relative Policy Optimization (GRPO). Our proposed method leverages BGE-M3 embeddings as a cost-efficient semantic-similarity reward, significantly reducing computational expenses up to 2.5x compared to an LLM-based reward model. Experiments on the NitiBench benchmark demonstrate substantial improvements: GRPO achieves up to 90% citation-F1 gains relative to the base model and a 31% increase in joint quality metrics over instruction tuning. Crucially, our approach provides a practical and effective solution for enhancing legal LLMs in resource-constrained environments.

Aligning LLMs for Thai Legal Question Answering with Efficient Semantic-Similarity Rewards

Large language models (LLMs) are moving into legal workflows, yet we lack a jurisdiction-grounded way to gauge their basic competence in thereof. We use India’s public legal examinations as a transparent proxy. Our multi-year benchmark assembles objective screens from top national and state exams and evaluates open and frontier LLMs under real world exam conditions. To probe beyond MCQs, we also include a lawyer-graded, paired-blinded study of long-form answers from the Supreme Court’s Advocate-on-Record exam. This is, to our knowledge, the first exam-grounded, India-specific yardstick for LLM court-readiness released with datasets and protocols. Our work shows that while frontier systems consistently clear historical cutoffs and often match or exceed recent top-scorer bands on objective exams, none surpasses the human topper on long-form reasoning. Grader notes converge on three reliability failure modes—procedural/format compliance, authority/citation discipline, and forum-appropriate voice/structure. These findings delineate where LLMs can assist (checks, cross-statute consistency, statute and precedent lookups) and where human leadership remains essential: forum-specific drafting and filing, procedural and relief strategy, reconciling authorities and exceptions, and ethical, accountable judgment.

Are LLMs Court-Ready? Evaluating Frontier Models on Indian Legal Reasoning

Definitions in alliance contracts play a critical role in shaping agreements, yet they can also lead to costly misunderstandings. This is exemplified by the multimillion-dollar AstraZeneca-Euopean Commission (EC) dispute, where the interpretation of `best reasonable effort' became the focal point of contention. In this interdisciplinary study, we leverage natural language processing (NLP) to systematically analyze patterns in the definitions included in alliance contracts. More specifically, we categorize the content of definitions into topics, identify common terms versus outliers that are semantically dissimilar and infrequently used, and track how definitions evolve over time. Analyzing a dataset of 380,131 definitions from 12,468 alliance contracts in the biopharmaceutical industry, we distinguish that definitions span legal, technological, and social topics, with social terms showing the highest dissimilarity across contracts. Using dynamic topic modeling, we explore how the content of definitions has shifted over two decades (2000–2020) and identify prevalent trends suggesting that contractual definitions reflect broader economic contexts. Notably, our results reveal that the AstraZeneca-EC dispute arose from an outlier—a highly unusual definition—that could have been flagged using NLP. Overall, these findings highlight the potential of data-driven approaches to uncover patterns in alliance contracts.

Tracing Definitions: Lessons from Alliance Contracts in the Biopharmaceutical Industry

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.

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