China

High-quality multilingual training data is essential for effectively pretraining large language models (LLMs). Yet, the availability of suitable open-source multilingual datasets remains limited. Existing state-of-the-art datasets mostly rely on heuristic filtering methods, restricting both their cross-lingual transferability and scalability. Here, we introduce JQL, a systematic approach that efficiently curates diverse and high-quality multilingual data at scale while significantly reducing computational demands. JQL distills LLMs&#39; annotation capabilities into lightweight annotators based on pretrained multilingual embeddings. These models exhibit robust multilingual and cross-lingual performance, even for languages and scripts unseen during training. Evaluated empirically across 35 languages, the resulting annotation pipeline substantially outperforms current heuristic filtering methods like Fineweb2. JQL notably enhances downstream model training quality and increases data retention rates. Our research provides practical insights and valuable resources for multilingual data curation, raising the standards of multilingual dataset development.

EMNLP 2025

Judging Quality Across Languages: A Multilingual Approach to Pretraining Data Filtering with Language Models

llms as quality judges

multilingual pre-training data filtering

cross-lingual generalization

multilingual llms

## 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.

High-quality multilingual training data is essential for effectively pretraining large language models (LLMs). Yet, the availability of suitable open-source multilingual datasets remains limited. Existing state-of-the-art datasets mostly rely on heuristic filtering methods, restricting both their cross-lingual transferability and scalability. Here, we introduce JQL, a systematic approach that efficiently curates diverse and high-quality multilingual data at scale while significantly reducing computational demands. JQL distills LLMs' annotation capabilities into lightweight annotators based on pretrained multilingual embeddings. These models exhibit robust multilingual and cross-lingual performance, even for languages and scripts unseen during training. Evaluated empirically across 35 languages, the resulting annotation pipeline substantially outperforms current heuristic filtering methods like Fineweb2. JQL notably enhances downstream model training quality and increases data retention rates. Our research provides practical insights and valuable resources for multilingual data curation, raising the standards of multilingual dataset development.

technical paper

We present Prolog-MATH, a curated corpus designed to support mathematical reasoning in large language models (LLMs) through logic programming. Each verbal math problem in the dataset is paired with a chain-of-thought explanation to generate Prolog program via a two-stage automated pipeline. In the first stage, an LLM (e.g., Deepseek-V3) predicts a set of relevant mathematical predicates that could be useful in solving the problem. In the second stage, the LLM uses these suggested predicates along with the expected answer type to generate a complete Prolog program. To improve coverage, we fine-tune an open-source LLM using supervised fine-tuning, followed by GRPO (Group Relative Policy Optimization) training to address problems that Deepseek-V3 fails to solve. To support this training, we propose a predicate-aware reward function that evaluates how well the generated solution incorporates the suggested predicates, complementing the standard binary reward. Experimental results show that: 1) Our two-stage pipeline achieves 70% solution coverage on the MATH training set; 2) GRPO training with the predicate-aware reward function enables a Qwen-3B-Instruct model to correctly solve additional problems missed by Deepseek-V3, further increasing solution coverage.

Predicate-Guided Generation for Mathematical Reasoning

We introduce ComplexTempQA,a large-scale dataset consisting of over 100 million question-answer pairs designed to tackle the challenges in temporal question answering. ComplexTempQA significantly surpasses existing benchmarks in scale and scope. Utilizing Wikipedia and Wikidata, the dataset covers questions spanning over two decades and offers an unmatched scale. We introduce a new taxonomy that categorizes questions as \textit{attributes}, \textit{comparisons}, and \textit{counting} questions, revolving around events, entities, and time periods, respectively. A standout feature of ComplexTempQA is the high complexity of its questions, which demand reasoning capabilities for answering such as across-time comparison, temporal aggregation, and multi-hop reasoning involving temporal event ordering and entity recognition. Additionally, each question is accompanied by detailed metadata, including specific time scopes, allowing for comprehensive evaluation of temporal reasoning abilities of large language models.

ComplexTempQA: A 100m Dataset for Complex Temporal Question Answering

Although current mainstream pre-trained large models, such as LLM models represented by ChatGPT and VLA models represented by OpenVLA, have achieved significant progress in multimodal tasks through a "Multiple-Input, Single-Output" (MISO) architecture. However, our investigation reveals that the MISO architecture exhibits fundamental limitations in "Multiple-Input, Multiple-Output" (MIMO) (e.g., parallel multi-tasks output processing): the architecture generates task mutual exclusion effects, leading to resource contention among different tasks when sharing output channels, and consequently resulting in optimization imbalance and performance. In contrast, human MIMO processing inherently enables concurrent task execution (e.g., while dialogue and decision-making) without interference. Inspired by this, in this work, we propose a unified MIMO training model with parallel multi-tasks output capabilities—the Visual Language Action Model for Simultaneously Chatting and Decision Making (VLASCD). We evaluate the model on the CARLA autonomous driving platform. The results show that, compared to LLM models with MISO dialogue capabilities, reinforcement learning models, and VLA models with MISO decision-making capabilities, VLASCD significantly outperforms existing MISO models in simultaneously handling dialogue generation and decision-making tasks within the MIMO scenario.

VLASCD: A Visual Language Action Model for Simultaneous Chatting and Decision Making

The ability to understand and answer questions over documents can be useful in many business and practical applications. However, documents often contain lengthy and diverse multimodal contents such as texts, figures, and tables, which are very time-consuming for humans to read thoroughly. Hence, there is an urgent need to develop effective and automated methods to aid humans in this task. In this work, we introduce M-LongDoc, a benchmark of 851 samples, and an automated framework to evaluate the performance of large multimodal models. We further propose a retrieval-aware tuning approach for efficient and effective multimodal document reading. Compared to existing works, our benchmark consists of more recent and lengthy documents with hundreds of pages, while also requiring open-ended explanations and not just extractive answers. To our knowledge, our training framework is the first to directly address the retrieval setting for multimodal long documents. To enhance open models, we construct a training corpus in a fully automatic manner. Experiments show that our tuning approach significantly improves the correctness of model responses by 4.6%.

M-LongDoc: A Benchmark For Multimodal Super-Long Document Understanding And A Retrieval-Aware Tuning Framework

Recent advances in text-only “slow-thinking” reasoning have prompted efforts to transfer this capability to vision-language models (VLMs), for training visual reasoning models (VRMs). However, such transfer faces critical challenges: Effective "slow thinking" in VRMs requires visual reflection, the ability to check the reasoning process based on visual information. Through quantitative analysis, we observe that current VRMs exhibit limited visual reflection, as their attention to visual information diminishes rapidly with longer generated responses. To address this challenge, we propose a new VRM Reflection-V, which enhances visual reflection based on reasoning data construction for cold-start and reward design for reinforcement learning (RL). Firstly, we construct vision-centered reasoning data by leveraging an agent that interacts between VLMs and reasoning LLMs, enabling cold-start learning of visual reflection patterns. Secondly, a visual attention based reward model is employed during RL to encourage reasoning based on visual information. Therefore, Reflection-V demonstrates significant improvements across multiple visual reasoning benchmarks. Furthermore, Reflection-V maintains a stronger and more consistent reliance on visual information during visual reasoning, indicating effective enhancement in visual reflection capabilities.

Look Again, Think Slowly: Enhancing Visual Reflection in Vision-Language Models

In recent years, architectural design automation has made significant progress, but the complexity of open-world environments continues to make residential design a challenging task, often requiring experienced architects to perform multiple iterations and human-computer interactions. Therefore, assisting ordinary users in navigating these complex environments to generate and edit residential design is crucial. In this paper, we present the CARD framework, which leverages a system of specialized cross-modal agents to adapt to complex open-world environments. The framework includes a point-based cross-modal information representation (CMI-P) that encodes the geometry and spatial relationships of residential rooms, a cross-modal residential generation model that acts as the lead designer to create standardized floor plans, and an embedded expert knowledge base for evaluating whether the designs meet user requirements and residential codes, providing feedback accordingly. Finally, a 3D rendering module assists users in visualizing and understanding the layout. CARD enables cross-modal residential generation from free-text input, empowering users to adapt to complex environments without requiring specialized expertise.

CARD: Cross-modal Agent Framework for Generative and Editable Residential Design

Decoder-only large language models typically rely solely on masked causal attention, which limits their expressiveness by restricting information flow to one direction. We propose Bitune, a method that enhances pretrained decoder-only LLMs by incorporating bidirectional attention into prompt processing. We evaluate Bitune in instruction-tuning and question-answering settings, showing significant improvements in performance on commonsense reasoning, arithmetic, and language understanding tasks. Furthermore, extensive ablation studies validate the role of each component of the method, and demonstrate that Bitune is compatible with various parameter-efficient finetuning techniques and full model finetuning.

Bitune: Leveraging Bidirectional Attention to Improve Decoder-Only LLMs

Ambiguity remains a fundamental challenge in Natural Language Processing (NLP) due to the inherent complexity and flexibility of human language. With the advent of Large Language Models (LLMs), addressing ambiguity has become even more critical due to their expanded capabilities and applications. In the context of Conversational Question Answering (CQA), this paper explores the definition, forms, and implications of ambiguity for language driven systems, particularly in the context of LLMs. We define key terms and concepts, categorize various disambiguation approaches enabled by LLMs, and provide a comparative analysis of their advantages and disadvantages. We also explore publicly available datasets for benchmarking ambiguity detection and resolution techniques and highlight their relevance for ongoing research. Finally, we identify open problems and future research directions, proposing areas for further investigation. By offering a comprehensive review of current research on ambiguities and disambiguation with LLMs, we aim to contribute to the development of more robust and reliable language systems.

Disambiguation in Conversational Question Answering in the Era of LLMs and Agents: A Survey

Using effective generalization capabilities of vision language models (VLMs) in context-specific dynamic tasks for embodied artificial intelligence remains a significant challenge. Although supervised fine-tuned models can better align with the real physical world, they still exhibit sluggish responses and hallucination issues in dynamically changing environments, necessitating further alignment. Existing post-SFT methods, reliant on reinforcement learning and chain-of-thought (CoT) approaches, are constrained by sparse rewards and action-only optimization, resulting in low sample efficiency, poor consistency, and model degradation. To address these issues, this paper proposes Thought-Centric Preference Optimization (TCPO) for effective embodied decision-making. Specifically, TCPO introduces a stepwise preference-based optimization approach, transforming sparse reward signals into richer step sample pairs. It emphasizes the alignment of the model's intermediate reasoning process, mitigating the problem of model degradation. Moreover, by incorporating Action Policy Consistency Constraint (APC), it further imposes consistency constraints on the model output. Experiments in the ALFWorld environment demonstrate an average success rate of **26.67%**, achieving a **6%** improvement over RL4VLM and validating the effectiveness of our approach in mitigating model degradation after fine-tuning. These results highlight the potential of integrating preference-based learning techniques with CoT processes to enhance the decision-making capabilities of vision-language models in embodied agents.

TCPO: Thought-Centric Preference Optimization for Effective Embodied Decision-making

Implicit reasoning is the ability of a language model to solve multi-hop reasoning tasks in a single forward pass, without chain of thought. We investigate this capability using GPT2-style language models trained from scratch on controlled k-hop reasoning datasets (k = 2, 3, 4). We show that while such models can indeed learn implicit k-hop reasoning, the required training data grows exponentially in k, and the required number of transformer layers grows linearly in k. We offer a theoretical explanation for why this depth growth is necessary. We further find that the data requirement can be mitigated, but not eliminated, through curriculum learning.

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