China

Short video platforms are evolving rapidly, making the identification of inappropriate content increasingly critical.
Existing approaches typically train separate and small classification models for each type of issue, which requires extensive human-labeled data and lacks cross-issue generalization.
We propose a reasoning-enhanced multimodal large language model (MLLM) pretraining paradigm for unified inappropriate content detection. To address the distribution gap between short video content and the original pretraining data of MLLMs, as well as the complex issue definitions, we introduce three targeted pretraining tasks:
(1) \textit{Caption}, to enhance the MLLM&#39;s perception of video details;
(2) \textit{Visual Question Answering (VQA)}, to deepen the MLLM&#39;s understanding of issue definitions and annotation guidelines;
(3) \textit{Chain-of-Thought (CoT)}, to enhance the MLLM&#39;s reasoning capability.
Experimental results show that our pretraining approach significantly improves the MLLM&#39;s performance in both zero-shot and supervised fine-tuning (SFT) settings.
In addition, our pretrained model demonstrates strong generalization capabilities to emergent, previously unseen issues.

EMNLP 2025

Reasoning-Enhanced Domain-Adaptive Pretraining of Multimodal Large Language Models for Short Video Content Moderation

Short video platforms are evolving rapidly, making the identification of inappropriate content increasingly critical.
Existing approaches typically train separate and small classification models for each type of issue, which requires extensive human-labeled data and lacks cross-issue generalization.
We propose a reasoning-enhanced multimodal large language model (MLLM) pretraining paradigm for unified inappropriate content detection. To address the distribution gap between short video content and the original pretraining data of MLLMs, as well as the complex issue definitions, we introduce three targeted pretraining tasks:
(1) \textit{Caption}, to enhance the MLLM's perception of video details;
(2) \textit{Visual Question Answering (VQA)}, to deepen the MLLM's understanding of issue definitions and annotation guidelines;
(3) \textit{Chain-of-Thought (CoT)}, to enhance the MLLM's reasoning capability.
Experimental results show that our pretraining approach significantly improves the MLLM's performance in both zero-shot and supervised fine-tuning (SFT) settings.
In addition, our pretrained model demonstrates strong generalization capabilities to emergent, previously unseen issues.

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.

Natural language interfaces (NLIs) democratize data analytics by enabling non-technical users to query relational databases via Text-to-SQL systems. While large language models (LLMs) have achieved state-of-the-art accuracy on benchmarks like Spider and BIRD, two critical challenges persist for real-time deployment: (1) inference latency due to sequential autoregressive decoding (e.g., 14.23–22.77 seconds per query for Qwen2.5-Coder-32B and Llama-70B on BIRD (Minidev)), and (2) schema hallucinations (e.g., invalid column references like customer_ids instead of cust_id). To address these, we propose Tree-Guided Token Decoding (TTD-SQL), a lightweight framework that integrates SQL grammar and database schema constraints into the decoding process without modifying the underlying LLM. TTD precomputes token-level decision trees over SQL keywords, table names, and column identifiers, enabling deterministic "auto-fill" transitions for uniquely determined tokens (e.g., "Singer_" → "ID") while retaining flexibility for unconstrained reasoning. Across five LLMs (CodeLlama, Phi-4, Qwen2.5, Granite, Llama-70B), TTD achieves up to 19.96% token-rate speedups by eliminating redundant forward passes (e.g., CodeLlama: 8.97→10.76 tokens/s on Spider) and reduces schema hallucinations by +17.7% in executable-SQL rates (e.g., CodeLlama on BIRD). By bridging rigid parser-based methods and flexible LLM generation, TTD offers a practical path toward reliable, high-performance SQL generation in both public benchmarks and enterprise settings.

TTD-SQL: Tree-Guided Token Decoding for Efficient and Schema-Aware SQL Generation

AI-generated clinical notes are increasingly used in healthcare, but evaluating their quality remains a challenge due to high subjectivity and limited scalability of expert review. Existing automated metrics often fail to align with real-world physician preferences. To address this, we propose a pipeline that systematically distills real user feedback into structured checklists for note evaluation. These checklists are designed to be interpretable, grounded in human feedback, and enforceable by LLM-based evaluators. Using deidentified data from over 21,000 clinical encounters (prepared in accordance with the HIPAA safe harbor standard) from a deployed AI medical scribe system, we show that our feedback-derived checklist outperforms baseline approaches in our offline evaluations in coverage, diversity, and predictive power for human ratings. Extensive experiments confirm the checklist's robustness to quality-degrading perturbations, significant alignment with clinician preferences, and practical value as an evaluation methodology. In offline research settings, our checklist offers a practical tool for flagging notes that may fall short of our defined quality standards.

From Feedback to Checklists: Grounded Evaluation of AI-Generated Clinical Notes

Large Language Models have achieved great success in tasks like sentiment analysis, machine translation, and question answering, yet their effectiveness in the multilingual financial domain remains less explored. This study explores the potential of generative LLMs for classifying financial sustainability in four diverse languages: English, Hindi, Bengali, and Telugu, representing low, medium, and high-resource language categories. We propose a novel fine-tuning approach that integrates both positive and negative rationales alongside classification labels. Unlike existing approaches, our method improves classification performance by incorporating structured bidirectional reasoning into financial decision-making. Extensive evaluations demonstrate that the proposed approach consistently outperforms prior methods across all four languages, establishing new benchmark results for multilingual financial NLP. Notably, it also enables smaller models to achieve competitive or even superior performance compared to significantly larger models fine-tuned with conventional methods, demonstrating its suitability for industry applications.

Bidirectional Reasoning Supervision for Multilingual Financial Decision Making

Repairing and maintaining car parts are crucial tasks in the automotive industry, requiring a mechanic to have all relevant technical documents available. However, retrieving the right documents from a huge database heavily depends on domain expertise and is time consuming and error-prone. By labeling available documents according to the components they relate to, concise and accurate information can be retrieved efficiently. However, this is a challenging task as the relevance of a document to a particular component strongly depends on the context and the expertise of the domain specialist. Moreover, component terminology varies widely between different manufacturers. We address these challenges by utilizing Large Language Models (LLMs) to enrich and unify a component database via web mining, extracting relevant keywords, and leveraging hybrid search and LLM-based re-ranking to select the most relevant component for a document. We systematically evaluate our method using various LLMs on an expert-annotated dataset and demonstrate that it outperforms the baselines, which rely solely on LLM prompting.

Automotive Document Labeling Using Large Language Models

Large Language Models (LLMs) are widely used in industry but remain prone to hallucinations, limiting their reliability in critical applications. This work addresses hallucination reduction in consumer grievance chatbots built using LLaMA 3.1 8B Instruct, a compact model frequently used in industry. We develop **HalluDetect**, an LLM-based hallucination detection system that achieves an F1 score of **69\%** outperforming baseline detectors by **25.44\%**. Benchmarking five chatbot architectures, we find that out of them, AgentBot minimizes hallucinations to **0.4159** per turn while maintaining the highest token accuracy (**96.13\%**), making it the most effective mitigation strategy. Our findings provide a scalable framework for hallucination mitigation, demonstrating that optimized inference strategies can significantly improve factual accuracy. While applied to consumer law, our approach generalizes to other high-risk domains, enhancing trust in LLM-driven assistants. We will release the code and dataset.

HalluDetect: Detecting, Mitigating, and Benchmarking Hallucinations in Conversational Systems in the Legal Domain

Deploying Large Language Models (LLMs) for question answering (QA) over lengthy contexts is a significant challenge. In industrial settings, this process is often hindered by high computational costs and latency, especially when multiple questions must be answered based on the same context. In this work, we explore the capabilities of LLMs to answer multiple questions based on the same conversational context. We conduct extensive experiments and benchmark a range of both proprietary and public models on this challenging task. Our findings highlight that while strong proprietary LLMs like GPT-4o achieve the best overall performance, fine-tuned public LLMs with up to 8 billion parameters can surpass GPT-4o in accuracy, which demonstrates their potential for transparent and cost-effective deployment in real-world applications.

How Accurate Are LLMs at Multi-Question Answering on Conversational Transcripts?

Large language models (LLMs) are applied to reasoning and (automated) planning across diverse domains, from travel itineraries to embodied AI tasks. However, concerns have been raised about their suitability for long-horizon tasks involving multiple constraints, as they are prone to hallucinations, particularly in adversarial scenarios. Safety reasoning also becomes critical for embodied AI agents, which interact with their physical environments to complete tasks on behalf of humans. However, existing (safety) benchmarks fail to represent a diverse range of multi-constraint tasks that require long-horizon planning with a focus on safety. To address this, we propose VestaBench, a benchmark curated using VirtualHome and BEHAVIOR-100. Our VestaBench includes (1) tasks that can be achieved safely under adversarial and multi-constraint settings, as well as (2) adversarial instructions that the agent must avoid. Our experiments with state-of-the-art LLM-based baselines reveal that they perform poorly against our tasks, not only achieving low success rates but also suffering significantly compromised safety outcomes. This observation reinforces the limitations of LLMs in generating safe plans when faced with adversarial settings or instructions. Finally, we believe that our VestaBench provides a valuable set of embodied tasks and challenges to the research and industry communities.

VestaBench: An Embodied Benchmark for Safe Long-Horizon Planning Under Multi-Constraint and Adversarial Settings

This paper introduces ECHO-LLaMA, an efficient LLaMA architecture designed to improve both the training speed and inference throughput of LLaMA architectures while maintaining its learning capacity. ECHO-LLaMA transforms LLaMA models into shared KV caching across certain layers, significantly reducing KV computational complexity while maintaining or improving language performance. Experimental results demonstrate that ECHO-LLaMA achieves up to 77\% higher token-per-second throughput during training, up to 16\% higher Model FLOPs Utilization (MFU), and up to 14\% lower loss when trained on an equal number of tokens. Furthermore, on the 1.1B model, ECHO-LLaMA delivers approximately 7\% higher test-time throughput compared to the baseline. By introducing a computationally efficient adaptation mechanism, ECHO-LLaMA offers a scalable and cost-effective solution for pretraining and finetuning large language models, enabling faster and more resource-efficient training without compromising performance.

ECHO-LLaMA: Efficient Caching for High-Performance LLaMA Training

In clinical science, biomarkers are crucial indicators for early cancer detection, prognosis, and guiding personalized treatment decisions. Although critical, extracting biomarkers and their levels from clinical texts remains a complex and underexplored problem in natural language processing research. In this paper, we present BIOPSY, an end-to-end pipeline that integrates a domain-adapted biomarker entity recognition model, a relation extraction model to link biomarkers to their respective mutations, a biomarker-type classifier, and finally, a tailored algorithm to capture biomarker expression levels. Evaluated on 5,000 real-world clinical texts, our system achieved an overall F1 score of 0.86 for oncology and 0.87 for neuroscience domains. This reveals the ability of the pipeline to adapt across various clinical sources, including trial records, research papers, and medical notes, offering the first comprehensive solution for end-to-end, context-aware biomarker extraction and interpretation in clinical research.

BIOPSY - Biomarkers In Oncology: Pipeline for Structured Yielding

Inference optimizations such as quantization, pruning, format and datatype conversion, model export, and serialization can lead to functional degradations in language model task performance. While most efforts on performance recovery for deployment focus on robust quantization techniques, we focus on recovering model accuracies from any sources that degrade model weights, such as improper model serialization. In this work, we propose Recover-LoRA, a lightweight and dataset agnostic method to recover accuracy in degraded models. Recover-LoRA uses synthetic data and logit distillation to learn LoRA adapters on selective layers that facilitate aligning the degraded model to its full precision model. We investigate the utility of Recover-LoRA across a diverse set of small language models (SLMs), including models with varying attention architectures, multi-head attention (MHA) and group-query attention (GQA), as well as several evaluation datasets. Our results show that Recover-LoRA recovers model accuracies by 5-17\% on MHA and GQA SLMs.

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TTD-SQL: Tree-Guided Token Decoding for Efficient and Schema-Aware SQL Generation

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