China

Corporate history in corporate annual reports includes events related to organizational changes, which can provide useful cues for a comprehensive understanding of corporate actions.
However, extracting organizational changes requires identifying differences in companies before and after an event, raising concerns about whether existing information extraction systems can accurately capture the relations. 
This work introduces JaCorpTrack, a novel event extraction task designed to identify events related to organizational changes.
JaCorpTrack defines five event types related to organizational changes and is designed to identify the company names before and after each event, as well as the corresponding date.
Experimental results indicate that large language models (LLMs) exhibit notable disparities in performance across event types.
Our analysis reveals that these systems face challenges in identifying company names before and after events, and in interpreting event types expressed under ambiguous terminology.
We will publicly release our dataset and experimental code at \url{https://github.com/foo/bar}.

EMNLP 2025

JaCorpTrack: Corporate History Event Extraction for Tracking Organizational Changes

corporate actions

organizational changes

corporate history

event extraction

The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. 

AAAI 2021

poster

## 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.

Large language models (LLMs) can transform education, but their optimization for direct question-answering often undermines effective pedagogy which requires strategically withholding answers. To mitigate this, we propose an online reinforcement learning (RL)-based alignment framework that can quickly adapt LLMs into effective tutors using simulated student-tutor interactions by emphasizing pedagogical quality and guided problem-solving over simply giving away answers. We use our method to train a 7B parameter tutor model without human annotations which reaches similar performance to larger proprietary models like LearnLM. We introduce a controllable reward weighting to balance pedagogical support and student solving accuracy, allowing us to trace the Pareto frontier between these two objectives. Our models better preserve reasoning capabilities than single-turn SFT baselines and can optionally enhance interpretability through thinking tags that expose the model’s instructional planning. Code and models will be released publicly.

From Problem-Solving to Teaching Problem-Solving: Aligning LLMs with Pedagogy using Reinforcement Learning

Manual slide creation is labor-intensive and requires expert prior knowledge. Existing natural language-based LLM generation methods struggle to capture the visual and structural nuances of slide designs. To address this, we formalize the Reference Image to Slide Generation task and propose Slide2Code, the first benchmark with difficulty-tiered samples based on a novel Slide Complexity Metric. We introduce SlideCoder, a layout-aware, retrieval-augmented framework for generating editable slides from reference images. SlideCoder integrates a Color Gradient-based Segmentation algorithm and a Hierarchical Retrieval-Augmented Generation method to decompose complex tasks and enhance code generation. We also release SlideMaster, a 7B open-source model fine-tuned with improved reverse-engineered data. Experiments show that SlideCoder outperforms state-of-the-art baselines by up to 40.5 points, demonstrating strong performance across layout fidelity, execution accuracy, and visual consistency. Our code is available at https://anonymous.4open.science/r/SlideCoder.

SlideCoder: Layout-aware RAG-enhanced Hierarchical Slide Generation from Design

Effectively resolving phonological ambiguities is crucial for robust natural language processing, as they are pervasive in tasks ranging from spelling correction to offensive language detection. However, current Large Language Models (LLMs) exhibit significant limitations when processing such ambiguities. To address this challenge, we present a framework that enhances LLMs' capability through a multiple-stage training approach. Our method begins with supervised fine-tuning on well-constructed datasets, which include three subtask datasets and a synthetic dataset of step-by-step reasoning chains. Following this, we implement reinforcement learning to incentivize phonetic reasoning in LLMs. Experimental results show that our method enables the base model to achieve comparable performance to a much larger model. Across progressive training phases, we identified that subtask datasets strengthen LLMs' foundational phonetic knowledge, while chain-of-thought training enhances complex reasoning and integrated application.

PhonoThink: Improving Large Language Models' Reasoning on Chinese Phonological Ambiguities

In this work, we introduce ConTEB (Context-aware Text Embedding Benchmark), a benchmark designed to evaluate retrieval models on their ability to leverage document-wide context. Our results show that state-of-the-art embedding models struggle in retrieval scenarios where context is required. To address this limitation, we propose InSeNT (In-sequence Negative Training), a novel contrastive post-training approach which combined with Late Chunking pooling enhances contextual representation learning while preserving computational efficiency. Our method significantly improves retrieval quality on ConTEB without sacrificing base model performance.
We further find chunks embedded with our method are more robust to suboptimal chunking strategies and larger retrieval corpus sizes.
We open-source all artifacts at https://github.com/illuin-tech/contextual-embeddings. 

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings

Culture is a rich and dynamic domain that evolves across both geography and time. However, existing studies on cultural understanding with vision-language models (VLMs) primarily emphasize geographic diversity, often overlooking the critical temporal dimensions. To bridge this gap, we introduce Hanfu-Bench, a novel, expert-curated multimodal dataset. Hanfu, a traditional garment spanning ancient Chinese dynasties, serves as a representative cultural heritage that reflects the profound temporal aspects of Chinese culture while remaining highly popular in Chinese contemporary society. Hanfu-Bench comprises two core tasks: cultural visual understanding and cultural image transcreation. The former task examines temporal-cultural feature recognition based on single- or multi-image inputs through multiple-choice visual question answering, while the latter focuses on transforming traditional attire into modern designs through cultural element inheritance and modern context adaptation. Our evaluation shows that closed VLMs perform comparably to non-experts on visual cutural understanding but fall short by 10% to human experts, while open VLMs lags further behind non-experts. For the transcreation task, multi-faceted human evaluation indicates that the best-performing model achieves a success rate of only 42%. Our benchmark provides an essential testbed, revealing significant challenges in this new direction of temporal cultural understanding and creative adaptation.

Hanfu-Bench: A Multimodal Benchmark on Cross-Temporal Cultural Understanding and Transcreation

Logical reasoning is essential for large language models (LLMs) to ensure accurate and coherent inference. However, LLMs struggle with reasoning order variations and fail to generalize across logically equivalent transformations. LLMs often rely on fixed sequential patterns rather than true logical understanding. To address this issue, we introduce an order-centric data augmentation framework based on commutativity in logical reasoning. We first randomly shuffle independent premises to introduce condition order augmentation. For reasoning steps, we construct a directed acyclic graph (DAG) to model dependencies between steps, which allows us to identify valid reorderings of steps while preserving logical correctness. By leveraging order-centric augmentations, models can develop a more flexible and generalized reasoning process. Finally, we conduct extensive experiments across multiple logical reasoning benchmarks, demonstrating that our method significantly enhances LLMs' reasoning performance and adaptability to diverse logical structures. We release our codes and augmented data in https://anonymous.4open.science/r/Order-Centric-Data-Augmentation-822C.

Order Doesn't Matter, But Reasoning Does: Training LLMs with Order-Centric Augmentation

The current Large Language Models (LLMs) face significant challenges in improving performance on low-resource languages and urgently need data-efficient methods. From the perspective of language-bridge, we propose BridgeX-ICL, a simple yet effective method to improve zero-shot Cross-lingual In-Context Learning (X-ICL) for low-resource languages. Unlike existing works focusing on language-specific neurons, BridgeX-ICL explores whether sharing neurons can improve cross-lingual performance in LLMs or not. We construct neuron probe data from the ground-truth MUSE bilingual dictionaries, and define a subset of language overlap neurons accordingly, to ensure full activation of these anchored neurons. Subsequently, we propose an HSIC-based metric to quantify LLMs-internal linguistic spectrum based on overlap neurons, which guides optimal bridge selection. The experiments conducted on 2 cross-lingual tasks and 15 language pairs from 7 diverse families (covering both high-low and moderate-low pairs) validate the effectiveness of BridgeX-ICL and offer empirical insights into the underlying multilingual mechanisms of LLMs.

Linguistic Neuron Overlap Patterns to Facilitate Cross-lingual Transfer on Low-resource Languages

Video understanding is essential for multimodal large language models (MLLMs) to interact effectively with users and the real world. However, analyzing long videos remains a major challenge due to the lack of high-quality video instruction data and effective training strategies. In this paper, we introduce a simple yet effective baseline for long-context video understanding, including dataset construction and training recipes. We curate a large-scale video instruction dataset with over 1M samples, encompassing videos from a few seconds to several minutes across diverse sources, without any human annotations. Additionally, we propose a progressive video instruction tuning strategy that incrementally increases input context length, enabling better utilization of videos of varying durations. Comprehensive experiments demonstrate that our dataset significantly outperforms existing video instruction datasets for fine-tuning MLLMs. Furthermore, our training approach establishes a strong video MLLM baseline, surpassing previous open-source models on video benchmarks and outperforming proprietary models like GPT-4V and GPT-4o-mini on VideoMME, even with a compact 7B model.

ProLongVid: A Simple but Strong Baseline for Long-context Video Instruction Tuning

To effectively support users' goal achievement in conventional chat-LLM services, providing user-centered follow-up questions is essential. However, existing studies primarily focus on enhancing information-seeking or topical relevance, often missing how follow-up questions can also satisfy users' intrinsic needs and conversational goals. To bridge this gap, we introduce FQ-Eval, a user-centered evaluation dataset designed for assessing follow-up question generation in chat-LLM services. FQ-Eval incorporates realistic chat-LLM usage scenarios and five distinct human-aligned criteria, each reflecting user expectations of effective follow-up questions. Experimental results show that the follow-up questions constructed through our approach clearly capture these criteria and closely align with human perceptions. This dataset thus enables robust, human-aligned follow-up question generation evaluation of various models and services.

FQ-Eval: Building Evaluation Dataset for User-centered Follow-up Question Generation

Language agents powered by large language models (LLMs) face significant deployment challenges in resource-constrained environments, particularly for specialized domains and less-common languages. This paper presents Tox-chat, a Korean chemical toxicity information agent devised within these limitations. We propose two key innovations: a context-efficient architecture that reduces token consumption through hierarchical section search, and a scenario-based dialogue generation methodology that effectively distills tool-using capabilities from larger models. Experimental evaluations demonstrate that our fine-tuned 8B parameter model substantially outperforms both untuned models and baseline approaches, in terms of DB faithfulness and preference. Our work offers valuable insights for researchers developing domain-specific language agents under practical constraints.

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