China

Continuous instruction following closely mirrors real-world tasks by requiring models to solve sequences of interdependent steps, yet existing multi-step instruction datasets suffer from three key limitations: (1) lack of logical coherence across turns, (2) narrow topical breadth and depth, and (3) reliance on rigid templates or heavy manual effort. We introduce LoCt-Pipeline, a novel pipeline that leverages modern LLMs’ reasoning capabilities to assemble rich, topic-related single-instruction data into multi-turn dialogues, producing chains that are logically coherent, progressively deepen in content, and span diverse domains without fixed templates or extensive human annotation. We employed this pipeline to construct LoCt-Instruct to assess models’ problem-solving abilities. The generated chains serve as a testbed for benchmarking a variety of models, including reasoning-oriented architectures, instruction-tuned variants, and state-of-the-art closed-source LLMs on their capacity to follow and correctly respond to each step. Our results reveal a substantial performance gap between current LLMs and human solvers. These findings highlight the need for more robust continuous instruction following. We will provide our dataset and pipeline upon acceptance.

EMNLP 2025

LoCt-Instruct: An Automatic Pipeline for Constructing Datasets of Logical Continuous Instructions

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*

[**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.

Developing a general-purpose system that can extract events with massive types is a long-standing target in Event Extraction (EE). In doing so, the basic challenge comes from the absence of an efficient and effective annotation framework to construct the corresponding datasets. In this paper, we propose an LLM-based collaborative annotation framework. Through collaboration among multiple LLMs and a subsequent voting process, it refines annotations of triggers from distant supervision and then carries out argument annotation. Finally, we create EEMT, the largest EE dataset to date, featuring over **200,000** samples, **3,465** event types, and **6,297** role types. Evaluation on human-annotated test set demonstrates that the proposed framework achieves the F1 scores of **90.1%** and **85.3%** for event detection and argument extraction, strongly validating its effectiveness. Besides, to alleviate the excessively long prompts caused by massive types, we propose an LLM-based Partitioning method for EE called LLM-PEE. It first recalls candidate event types and then splits them into multiple partitions for LLMs to extract. After fine-tuning on the EEMT training set, the distilled LLM-PEE with 7B parameters outperforms state-of-the-art methods by **5.4%** and **6.1%** in event detection and argument extraction. Besides, it also surpasses mainstream LLMs by **12.9%** on the unseen datasets, which strongly demonstrates the generalization capabilities of both the EEMT dataset and the LLM-PEE method.

Towards Event Extraction with Massive Types: LLM-based Collaborative Annotation and Partitioning Extraction

Entity Linking and Entity Disambiguation systems aim to link entity mentions to their corresponding entries, typically represented by descriptions within a predefined, static knowledge base. Current models assume that these knowledge bases are complete and up-to-date, rendering them incapable of handling entities not yet included therein. However, in an ever-evolving world, new entities emerge regularly, making these static resources insufficient for practical applications. To address this limitation, we introduce RAED, a model that retrieves external knowledge to improve factual grounding in entity descriptions. Using sources such as Wikipedia, RAED effectively disambiguates entities and bases their descriptions on factual information, reducing the dependence on parametric knowledge. Our experiments show that retrieval not only enhances overall description quality metrics, but also reduces hallucinations. Moreover, despite not relying on fixed entity inventories, RAED outperforms systems that require predefined candidate sets at inference time on Entity Disambiguation. Finally, we show that descriptions generated by RAED provide useful entity representations for downstream Entity Linking models, leading to improved performance in the extremely challenging Emerging Entity Linking task.

RAED: Retrieval-Augmented Entity Description Generation for Emerging Entity Linking and Disambiguation

This paper compares the effectiveness of traditional machine learning methods, encoder-based models, and large language models (LLMs) on the task of detecting depression and anxiety. Five Russian-language datasets were considered, each differing in format and in the method used to define the target pathology class. We tested AutoML models based on linguistic features, several variations of encoder-based Transformers such as BERT, and state-of-the-art LLMs as pathology classification models. The results demonstrated that LLMs outperform traditional methods, particularly on noisy and small datasets where training examples vary significantly in text length and genre. However, psycholinguistic features and encoder-based models can achieve performance comparable to language models when trained on texts from individuals with clinically confirmed depression, highlighting their potential effectiveness in targeted clinical applications.

Exploring Large Language Models for Detecting Mental Disorders

Almost 50% depression patients face the risk of going into relapse. The risk increases to 80% after the second episode of depression. Although, onset of depression detection from social media has attained considerable attention, depression relapse detection has remained largely unexplored. In this paper, we introduce the first relapse focused dataset of 201 Reddit users named ReDepress annotated by clinical psychologists. In contrast to prior work, we integrate insights from cognitive theories of depression-focusing on constructs such as rumination, attention bias, interpretation bias and memory bias to enrich both the annotation process and the predictive modeling approaches. We describe our data collection pipeline, annotation methodology, and initial analyses, revealing the correlation between cognitive factors and relapse indicators. Our preliminary findings underscore the importance of bridging computational methods with cognitive and clinical frameworks to better address relapse-a critical need in the ongoing fight against major depressive disorder.

ReDepress: A Cognitive Framework for Detecting Depression Relapse from Social Media

As digital platforms redefine educational paradigms, ensuring interactivity remains vital for effective learning. This paper explores using Multimodal Large Language Models (MLLMs) to automatically respond to student questions from online lectures - a novel question answering task of real world significance. We introduce the EduVidQA Dataset with 5252 question-answer pairs (both synthetic and real-world) from 296 computer science videos covering diverse topics and difficulty levels. To understand the needs of the dataset and task evaluation, we empirically study the qualitative preferences of students, which we provide as an important contribution to this line of work. Our benchmarking experiments consist of 6 state-of-the-art MLLMs, through which we study the effectiveness of our synthetic data for finetuning, as well as showing the challenging nature of the task. We evaluate the models using both text-based and qualitative metrics, thus showing a nuanced perspective of the models' performance, which is paramount to future work. This work not only sets a benchmark for this important problem, but also opens exciting avenues for future research in the field of Natural Language Processing for Education.

EduVidQA: Generating and Evaluating Long-form Answers to Student Questions based on Lecture Videos

Wikipedia is the largest open knowledge corpus, widely used worldwide and serving as a key resource for training large language models (LLMs) and retrieval-augmented generation (RAG) systems. Ensuring its accuracy is therefore critical. But how accurate is Wikipedia? In this paper, we focus on inconsistencies, a specific type of factual inaccuracy. We introduce the task of corpus-level inconsistency detection and present WikiCollide, a human-annotated dataset for this task. We also propose CLAIRE, an agent-based system combining an LLM with information retrieval to effectively identify inconsistencies, which outperforms strong LLM baselines by 2.1% in terms of AUROC on our dataset. Based on our findings, we estimate that at least 79.9 million facts (approximately 3.3%) in the English Wikipedia contradict at least one other fact within the corpus (99% confidence interval: 37.6 million to 121.9 million). We further show that these inconsistencies propagate into widely-used NLP datasets, affecting gold labels in at least 7.3% of examples in the fact-verification dataset FEVEROUS and 4.0% in the question-answering dataset AmbigQA. In a user study with experienced Wikipedia editors, 87.5% of participants reported increased confidence in identifying inconsistencies when using CLAIRE, discovering on average 64.7% more inconsistencies in the same amount of time. Our results demonstrate that LLM-based tools can effectively assist humans in detecting inconsistencies in large-scale corpora.

Detecting Corpus-Level Knowledge Inconsistencies in Wikipedia with Large Language Models

We introduce a novel Question Answering (QA) architecture that enhances the selection of answers by retrieving targeted supporting evidence. Unlike traditional systems that retrieve documents or passages relevant solely to a query q, our approach retrieves content relevant to the combination (q,a), focusing explicitly on the supporting relationship between the query and the answer a. By prioritizing this relational context, our method identifies paragraphs that directly substantiate the correctness of a for q, achieving higher accuracy compared to standard retrieval systems. Furthermore, we demonstrate that our neural retrieval approach efficiently scales to retrieve answer supports from hundreds of millions of paragraphs, setting a new benchmark in QA performance.

Retrieving Support to Rank Answers in Open-Domain Question Answering

Knowledge Graph Question Answering (KGQA) aims to answer natural language questions based on knowledge graphs. Recent approaches apply the Retrieval-Augmented Generation (RAG) paradigm to incorporate Large Language Models (LLMs) to this task, where a retriever selects a question-related subgraph and an LLM-based generator is then adopted to predict answers based on the retrieved subgraph. However, the subgraph selection process is non-differentiable, preventing end-to-end training of the retriever and the generator in these approaches, which leads to sub-optimal performance. To overcome this limitation, this paper proposes a Differentiable RAG (D-RAG) approach that jointly optimizes the retriever and the generator for KGQA. Via reformulating the optimization objective as an expectation over a subgraph distribution with respect to answer generation likelihood, D-RAG makes the joint optimization feasible. Specifically, it implements this joint optimization through a differentiable subgraph sampling and prompting module that integrates Gumbel-Softmax reparameterization for sampling and a neural prompt construction process that fuses semantic and structural information. Experimental results on WebQSP and CWQ demonstrate that D-RAG outperforms state-of-the-art approaches.

D-RAG: Differentiable Retrieval-Augmented Generation for Knowledge Graph Question Answering

Language models such as GPT and Llama have shown remarkable ability on diverse natural language tasks, yet their performance on complex table tasks (e.g., NL-to-Code, data cleaning, etc.) continue to be sub-optimal. To improve their performance, task-specific fine-tuning is often needed, which however require expensive human labeling, and is prone to over-fitting. In this work, we propose Table-Specialist, a new self-trained fine-tuning paradigm specifically designed for table tasks. Our insight is that for each table task, there often exist two dual versions of the same task, one generative and one classification in nature. Leveraging their duality, we propose a Generator-Validator paradigm, to iteratively generate-then-validate training data from language-models, to fine-tune stronger Table-Specialist models that can specialize in a given task, without using manually-labeled data. Extensive evaluations of Table-Specialist on Llama, GPT-3.5 and GPT-4 suggest that our Table-Specialist has (1) strong performance on diverse table tasks over vanilla language-models -- for example, Table-Specialist fine-tuned on GPT-3.5 not only outperforms vanilla GPT-3.5, but can often match or surpass GPT-4 level quality, (2) lower cost to deploy, because when Table-Specialist fine-tuned on GPT-3.5 achieve GPT-4 level quality, it becomes possible to deploy smaller models with lower latency and inference cost, with comparable quality, and (3) better generalizability when evaluated across multiple benchmarks, since Table-Specialist is fine-tuned on a broad range of training data systematically generated from diverse real tables. Our code, data, and technical report are available for future research.

Table-LLM-Specialist: Language Model Specialists for Tables using Iterative Fine-tuning

Large Language Models (LLMs), particularly smaller variants, still struggle with complex reasoning tasks. While inference-time prompting can guide reasoning, existing methods often rely on sequential queries. Ensemble approaches offer a promising path to performance gains, especially given recent batch inference speed-ups. This work introduces DIPPER, a novel, training-free framework that transforms a single LLM into an effective inference-time ensemble. By feeding the model an optimized and diverse set of prompts in parallel, DIPPER elicits varied reasoning paths, leading to performance gains. We empirically demonstrate significant improvements on mathematical reasoning benchmarks, such as MATH, where a DIPPER ensemble of three Qwen2-MATH-1.5B instances (via parallel prompting of a single model) outperforms a larger Qwen2-MATH-7B model.

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Towards Event Extraction with Massive Types: LLM-based Collaborative Annotation and Partitioning Extraction

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