China

Text anonymization is the process of removing or obfuscating information from textual data to protect the privacy of individuals. This process inherently involves a complex trade-off between privacy protection and information preservation, where stringent anonymization methods can significantly impact the text&#39;s utility for downstream applications. Evaluating the effectiveness of text anonymization proves challenging from both privacy and utility perspectives, as there is no universal benchmark that can comprehensively assess anonymization techniques across diverse, and sometimes contradictory contexts. We present Tau-Eval, an open-source framework for benchmarking text anonymization methods through the lens of privacy and utility task sensitivity. A Python library, code, documentation and tutorials are publicly available.

EMNLP 2025

Tau-Eval: A Unified Evaluation Framework for Useful and Private Text Anonymization

Text anonymization is the process of removing or obfuscating information from textual data to protect the privacy of individuals. This process inherently involves a complex trade-off between privacy protection and information preservation, where stringent anonymization methods can significantly impact the text's utility for downstream applications. Evaluating the effectiveness of text anonymization proves challenging from both privacy and utility perspectives, as there is no universal benchmark that can comprehensively assess anonymization techniques across diverse, and sometimes contradictory contexts. We present Tau-Eval, an open-source framework for benchmarking text anonymization methods through the lens of privacy and utility task sensitivity. A Python library, code, documentation and tutorials are publicly available.

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## 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.

This work introduces UnityAI-Guard, a framework for binary toxicity classification targeting low-resource Indian languages. While existing systems predominantly cater to high-resource languages, UnityAI-Guard addresses this critical gap by developing state-of-the-art models for identifying toxic content across diverse Brahmic/Indic scripts. Our approach achieves an impressive average F1-score of 84.23% across seven languages, leveraging a dataset of 567k training instances and 30k manually verified test instances. By advancing multilingual content moderation for linguistically diverse regions, UnityAI-Guard also provides public API access to foster broader adoption and application.

UnityAI Guard: Pioneering Toxicity Detection Across Low-Resource Indian Languages

We present SynthTextEval, a toolkit for conducting comprehensive evaluations of synthetic text. The fluency of large language model (LLM) outputs has made synthetic text potentially viable for numerous applications, such as reducing the risks of privacy violations in the development and deployment of AI systems in high-stakes domains. Realizing this potential, however, requires principled consistent evaluations of synthetic data across multiple dimensions: its utility in downstream systems, the fairness of these systems, the risk of privacy leakage, general distributional differences from the source text, and qualitative feedback from domain experts. SynthTextEval allows users to conduct evaluations along all of these dimensions over synthetic data that they upload or generate using the toolkit's generation module. While our toolkit can be run over any data, we highlight its functionality and effectiveness over datasets from two high-stakes domains: healthcare and law. By consolidating and standardizing evaluation metrics, we aim to improve the viability of synthetic text, and in-turn, privacy-preservation in AI development.

SynthTextEval: Synthetic Text Data Generation and Evaluation for High-Stakes Domains

Understanding when and how linguistic knowledge emerges during language model training remains a central challenge for interpretability. Most existing tools are post hoc, rely on scalar metrics, or require nontrivial integration effort, making comprehensive interpretability analysis difficult to deploy and maintain. We introduce TRACE, a modular toolkit for training and inference-time interpretability analysis of transformer models. It enables lightweight, in-training analysis of linguistic and representational signals, including features probing, intrinsic dimensionality, Hessian curvature, and output diagnostics. It integrates with ABSynth, a controllable synthetic corpus generator that provides structured annotations for precise evaluation of linguistic feature acquisition. Experiments with autoregressive transformers demonstrate that TRACE reveals developmental phenomena such as early syntactic emergence, delayed semantic acquisition, and representational compression, signals overlooked by traditional scalar metrics such as loss or accuracy. With minimal integration effort, the tool enables layer-wise diagnostics, convergence-based early stopping, and detection of structural errors, making transformer analysis interpretable, actionable, and reproducible.

TRACE: Training and Inference-Time Interpretability Analysis for Language Models

While machine translation has taken great strides in recent years, thanks in large part to transformer language models, machine translation tools are designed primarily for plain text, and thus not equipped to deal with complex markup documents. Not even Large Language Models can reliably handle LaTeX source files, as non-standard structures are not captured by any available training data. Previous attempts to create translation engines for LaTeX either work on compiled documents, rely on document pre-processors which may lose critical semantic elements, or cannot distinguish between text and non-text content. In this paper we present LaTeXMT, a software solution for structure-preserving, source-to-source translation of LaTeX documents. All of the source code to LaTeXMT is provided under the LGPL-3.0 open-source licence and a web version is publicly available.

LaTeXMT: Machine Translation for LaTeX Documents

Large language models (LLMs) are advancing at an unprecedented pace globally, with regions increasingly adopting these models for applications in their primary language. Evaluation of these models in diverse linguistic environments, especially in low-resource languages, has become a major challenge for academia and industry. Existing evaluation frameworks suffer from inconsistency across different benchmarks, being disproportionately focused on English and a handful of high-resource languages, thereby overlooking the realistic performance of LLMs in multilingual and lower-resource scenarios. To address this critical challenge of fragmented and inconsistent multilingual evaluation, we introduce GlotEval, a unified and lightweight framework that systematically integrates over 20 benchmarks under a standardized ISO 639-3 language identifier system, allowing for seamless incorporation of new benchmarks. Supporting nine key tasks (machine translation, text classification, summarization, open-ended generation, reading comprehension, sequence labeling, intrinsic evaluation, instruction following and reasoning), spanning over dozens to hundreds of languages, GlotEval uniquely enables language-specific, cross-benchmark analysis and non-English-centric evaluations at a scale previously less practical for many researchers. This enables a precise diagnosis of model strengths and weaknesses in diverse linguistic contexts. A multilingual translation case study demonstrates GlotEval's applicability for multilingual and language-specific evaluations.

GlotEval: A Test Suite for Massively Multilingual Evaluation of Large Language Models

Empathetic interaction is a cornerstone of human-machine communication, due to the need for understanding speech enriched with paralinguistic cues and generating emotional and expressive responses. However, the most powerful empathetic LSLMs are increasingly closed off, leaving the crucial details about the architecture, data and development opaque to researchers. Given the critical need for transparent research into the LSLMs and empathetic behavior, we present OpenS2S, a fully open-source, transparent and end-to-end LSLM designed to enable empathetic speech interactions. Based on our empathetic speech-to-text model BLSP-Emo, OpenS2S further employs a streaming interleaved decoding architecture to achieve low-latency speech generation. To facilitate end-to-end training, OpenS2S incorporates an automated data construction pipeline that synthesizes diverse, high-quality empathetic speech dialogues at low cost. By leveraging large language models to generate empathetic content and controllable text-to-speech systems to introduce speaker and emotional variation, we construct a scalable training corpus with rich paralinguistic diversity and minimal human supervision. We release the fully open-source OpenS2S model, including the dataset, model weights, pre-training and fine-tuning codes, to empower the broader research community and accelerate innovation in empathetic speech systems.

OpenS2S: Advancing Fully Open-Source End-to-End Empathetic Large Speech Language Model

Automatic research with Large Language Models (LLMs) is rapidly gaining importance, driving the development of increasingly complex workflows involving multi-agent systems, planning, tool usage, and human-agent interaction to accelerate research processes. However, as more researchers begin to use and build upon these tools and platforms, the complexity and difficulty of extending and maintaining such agentic workflows has become a significant challenge, particularly as algorithms and architectures continue to advance. To address this growing complexity, TinyScientist identifies the essential components of the automatic research workflow and proposes an interactive, extensible, and controllable framework that adapts easily to new tools and supports iterative growth. We have developed an interactive UI and a robust Python package to make state-of-the-art auto-research pipelines accessible to all researchers and developers. TinyScientist is publicly available and actively maintained at https://github.com/ulab-uiuc/tiny-scientist, with over 100 stars and 10 forks on GitHub. Its Python package is released on PyPI at https://pypi.org/project/tiny-scientist/ and has been downloaded more than 700 times.

TinyScientist: An Interactive, Extensible, and Controllable Framework for Building Research Agents

In this paper, we introduce ECom-Bench, the first benchmark framework for evaluating LLM agent with multimodal capabilities in the e-commerce customer support domain. ECom-Bench features dynamic user simulation based on persona information collected from real e-commerce customer interactions and a realistic task dataset derived from authentic e-commerce dialogues. These tasks, covering a wide range of business scenarios, are designed to reflect real-world complexities, making ECom-Bench highly challenging. For instance, even advanced models like GPT-4o achieve only a 10–20\% pass^3 metric in our benchmark, highlighting the substantial difficulties posed by complex e-commerce scenarios. Upon publication, the code and data will be open-sourced

ECom-Bench: Can LLM Agent Resolve Real-World E-commerce Customer Support Issues?

Neural models for Japanese pronunciation estimation often suffer from errors such as
hallucinations (generating pronunciations that are not grounded in the input) and 
omissions (skipping parts of the input).
Although attention-based alignment has been used to detect such errors,
selecting reliable attention heads is difficult,
and developing methods that can both detect and correct these errors
remains challenging.


In this paper, we propose a simple method called
existence-based alignment check.
In this approach,
we consider alignment candidates
independently extracted from all attention heads,
and check whether at least one of these candidates satisfies two conditions
derived from the linguistic properties of Japanese pronunciation:
monotonicity and pronunciation length per character.
We generate multiple hypotheses using beam search
and use the alignment check as a filtering mechanism
to correct hallucinations and omissions.


We apply this method to a dataset of Japanese facility names
and demonstrate that it improves pronunciation estimation accuracy
by over 2.5\%.

Just One is Enough: An Existence-based Alignment Check for Robust Japanese Pronunciation Estimation

The reasoning capabilities of Large Language Models (LLMs) play a critical role in many downstream tasks, yet depend strongly on the quality of training data. Despite various proposed data construction methods, their practical utility in real-world pipelines remains underexplored. In this work, we conduct a comprehensive analysis of open-source datasets and data synthesis techniques for mathematical reasoning, evaluating them under a unified pipeline designed to mirror training and deployment scenarios. We further distill effective data selection strategies and identify practical methods suitable for industrial applications. Our findings highlight that structuring data in more interpretable formats, or distilling from stronger models often outweighs simply scaling up data volume. This study provides actionable guidance for integrating training data to enhance LLM capabilities, supporting both cost-effective data curation and scalable model enhancement. We hope this work will inspire further research on how to balance "more data" versus "better data" for real-world reasoning tasks.

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UnityAI Guard: Pioneering Toxicity Detection Across Low-Resource Indian Languages

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