China

The specialized vocabulary and nuanced concepts of the telecommunications industry pose persistent challenges for standard Natural Language Processing (NLP) models. Generic embedding models often struggle to represent telecom-specific semantics, limiting their utility in retrieval and downstream tasks. We present T-VEC (Telecom Vectorization Model), a domain-adapted embedding model fine-tuned from the gte-Qwen2-1.5B-instruct backbone using a triplet loss objective over 100K curated telecom triplets. T-VEC sets a new benchmark in telecom retrieval, achieving CosineSim@1 of 0.8814, Recall@5 of 0.9249, and Top1 Exact Match of 0.9310—significantly outperforming leading general-purpose models like MPNet, BGE, and E5 by 20-30\% relative margin. These gains confirm T-VEC’s superior domain grounding and retrieval precision, with embedding visualizations further showcasing tight clustering of telecom-relevant concepts. We release T-VEC and its tokenizer to support more robust and semantically faithful NLP applications within the telecom domain.

EMNLP 2025

T-VEC: A Telecom-Specific Vectorization Model with Enhanced Semantic Understanding via Deep Triplet Loss Fine-Tuning

domain-specific embeddings

telecommunications

natural language processing

## Welcome to EMNLP 2022!
I am delighted to welcome you to EMNLP 2022! I believe this conference has been complicated beyond any precedent. Over the past year, it’s been thrilling to see the organization team approach each new puzzle with creativity and enthusiasm. We hope that those participating in Abu Dhabi as well as those joining remotely will leave the conference feeling newly inspired by the program and newly connected to our ever-growing community. Following EMNLP 2021 and major NLP conferences since, EMNLP 2022 is “hybrid,” serving both virtual and in-person participants.

Our key innovations for EMNLP 2022 include:

* EMNLP 2022 is “hybrid” in a second sense, as well: we allowed both direct and rolling review paper submissions, building on the pilot experiment of EMNLP 2021, which considered a small number of ARR submissions. 
* Familiar from NAACL but new to EMNLP, we’ve added an industry track.
* During the conference, “portals” will link virtual poster sessions to in-person conference participants during poster sessions each day.
* The first ACL-family conference in the United Arab Emirates.

 *Message from Noah A. Smith, University of Washington and Allen Institute for AI, Seattle, Washington, USA* 
***EMNLP 2022 General Chair***
 
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EMNLP 2022

Welcome!
EMNLP 2022 will take place in Abu Dhabi from December 7th to December 11th, 2022. And it will be held in hybrid mode, both online and offline.

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." 

*Message from the General Chair, Dirk Hovy*

Celebrate 30 Years of EMNLP! 
EMNLP 2025 will be held in Suzhou, China from November 5th to November 9th, 2025.

Cascade systems for open-ended text generation face a fundamental challenge: determining output reliability when generation quality lies on a continuous spectrum, often with multiple valid responses. To address this, we propose _semantic agreement_—meaning-level consensus between ensemble outputs—as a training-free signal for reliable deferral. We show that when diverse model outputs agree semantically, their consensus is a stronger reliability signal than token-level confidence. Evaluated from 500M to 70B-parameter models, semantic cascades improve deferral accuracy, match or surpass target-model quality at 40\% of the cost, and reduce latency by up to 60\%. Our method requires no model internals, works across black-box APIs, and remains robust to model updates, making it a practical baseline for real-world LLM deployment.

Semantic Agreement Enables Efficient Open-Ended LLM Cascades

Large Language Models (LLMs) have transformed natural language processing, yet they still struggle with direct text editing tasks that demand precise, context-aware modifications. While models like ChatGPT excel in text generation and analysis, their editing abilities often fall short, addressing only superficial issues rather than deeper structural or logical inconsistencies. In this work, we introduce a dual approach to enhance LLMs editing performance. First, we present InstrEditBench, a high-quality benchmark dataset comprising over 20,000 structured editing tasks spanning Wiki articles, LaTeX documents, code, and database Domain-specific Languages (DSL). InstrEditBench is generated using an innovative automated workflow that accurately identifies and evaluates targeted edits, ensuring that modifications adhere strictly to specified instructions without altering unrelated content. Second, we propose FineEdit, a specialized model trained on this curated benchmark. Experimental results demonstrate that FineEdit achieves significant improvements around 10% compared with Gemini on direct editing tasks, convincingly validating its effectiveness.

Bridging the Editing Gap in LLMs: FineEdit for Precise and Targeted Text Modifications

We present an Audio-Visual Language Model (AVLM) for expressive speech generation by integrating full-face visual cues into a pre-trained expressive speech model. We explore multiple visual encoders and multimodal fusion strategies during pre-training to identify the most effective integration approach. Subsequent fine-tuning on emotion recognition and expressive dialogue tasks yields substantial gains over speech-only baselines (e.g., +5 F1 in emotion recognition). AVLM highlights the value of expressive visual information in guiding speech generation and offers a foundation for end-to-end multimodal conversational systems.

Seeing is Believing: Emotion-Aware Audio-Visual Language Modeling for Expressive Speech Generation

Health misinformation spreading online poses a significant threat to public health. Researchers have explored methods for automatically generating counterspeech to health misinformation as a mitigation strategy. Existing approaches often produce uniform responses, ignoring that the health literacy level of the audience could affect the accessibility and effectiveness of counterspeech. We propose a Controlled-Literacy framework using retrieval-augmented generation (RAG) with reinforcement learning (RL) to generate tailored counterspeech adapted to different health literacy levels. In particular, we retrieve knowledge aligned with specific health literacy levels, enabling accessible and factual information to support generation. We design a reward function incorporating subjective user preferences and objective readability-based rewards to optimize counterspeech to the target health literacy level. Experiment results show that Controlled-Literacy outperforms baselines by generating more accessible and user-preferred counterspeech. This research contributes to more equitable and impactful public health communication by improving the accessibility and comprehension of counterspeech to health misinformation.

Speaking at the Right Level: Literacy-Controlled Counterspeech Generation with RAG-RL

Chain-of-Thought (CoT) prompting enables complex reasoning in large language models (LLMs), including applications in information retrieval (IR). However, it often leads to overthinking, where models produce excessively long and semantically redundant traces with little or no benefit. We identify two key challenges in IR: redundant trajectories that revisit similar states and misguided reasoning that diverges from user intent. To address these, we propose State Machine Reasoning (SMR), a transition-based reasoning framework composed of discrete actions (REFINE, RERANK, STOP) that support early stopping and fine-grained control. Experiments on the BEIR and BRIGHT benchmarks show that \ours improves retrieval performance (nDCG@10) by 3.4% while reducing token usage by 74.4%. It generalizes across LLMs and retrievers without requiring task-specific tuning, offering a practical alternative to conventional CoT reasoning.

From Token to Action: State Machine Reasoning to Mitigate Overthinking in Information Retrieval

Supervised fine-tuning (SFT) is a widely used and highly effective method for adapting Large Language Models (LLMs) to specific tasks. However, it often suffers from overfitting, causing models to excel on fine-tuned data but struggle with unseen or rare real-world inputs. While recent methods like Reinforcement Learning from Human Feedback (RLHF) and Reinforcement Learning with AI Feedback (RLAIF) aim to align LLMs with human values and tasks, they face challenges such as the high cost of human labeling or instabilities and biases inherent in using LLMs as judges. To address these issues, we propose a novel approach called Reinforcement Learning from supervised Alignment (RLA), which constructs a supervised alignment to train the reward model for reinforcement learning. Using only 100,000 MS MARCO samples, our method outperforms RLAIF by a relative margin ranging from +5.38% to +131.8%. It also significantly enhances the baseline Llama3 LLM, achieving up to +55% improvement on in-domain tasks and up to +16% on out-of-domain tasks. While RLA slightly underperforms supervised fine-tuning (SFT) on in-domain benchmarks, it surpasses SFT by up to 50 times on out-of-domain and cross-task evaluations, demonstrating strong generalization capabilities.

Reinforcement Learning with Supervised Alignment

Scientific evaluation of Large Language Models is an important topic that quantifies any degree of progress we make with new models. Even though current LLMs show high level of accuracy on benchmark datasets, the single-sample approach to evaluating them is not sufficient as it ignores high entropy of LLM responses. We introduce a Monte-Carlo evaluation framework for evaluating LLMs that follows behavioral science methodologies and provides statistical guarantees for estimates of performance. We test our framework on multiple LLMs to see if they are susceptible to cognitive biases. We find significant effect of prompts that induce cognitive biases in LLMs, raising questions about their reliability in social sciences and business. We also see higher susceptibility of newer and larger LLMs to cognitive biases, which shows a development towards more human-like and less rational LLM responses. We conclude by calling for the use of Monte-Carlo sampling as opposed to pass@1 for the broader LLM evaluations.

A Monte-Carlo Sampling Framework For Reliable Evaluation of Large Language Models Using Behavioral Analysis

Large Language Models (LLMs) as clinical agents require careful behavioral adaptation. While adept at reactive tasks (e.g., diagnosis reasoning), LLMs often struggle with proactive engagement, like unprompted identification of critical missing information or risks. We introduce **BehaviorBench**, a comprehensive dataset to evaluate agent behaviors across a clinical assistance spectrum, ranging from reactive query responses to proactive interventions (e.g., clarifying ambiguities, flagging overlooked critical data). Our BehaviorBench experiments reveal LLMs' inconsistent proactivity. To address this, we propose **BehaviorSFT**, a novel training strategy using behavioral tokens to explicitly condition LLMs for dynamic behavioral selection along this spectrum. BehaviorSFT boosts performance, achieving up to 97.3% overall Macro F1 on BehaviorBench and improving proactive task scores (e.g., from 95.0% to 96.5% for Qwen2.5-7B-Ins). Crucially, blind clinician evaluations confirmed BehaviorSFT-trained agents exhibit more realistic clinical behavior, striking a superior balance between helpful proactivity (e.g., timely, relevant suggestions) and necessary restraint (e.g., avoiding over-intervention) versus standard fine-tuning or explicit instructed agents.

BehaviorSFT: Behavioral Token Conditioning for Health Agents Across the Proactivity Spectrum

Distractor generation is the task of automatically generating plausible yet incorrect options (i.e., distractors) for fill-in-the-blank and multiple-choice questions. In assessment, distractors must be contextually relevant to the given question and answer. Even though recent research works focus on fine-tuning pre-trained encoder-decoder models with data augmentation techniques to generate distractors, these models often fail to capture the full semantic representation of a given question-answer and related distractors. The augmentation methods often rely on expanding the quantity of proposed candidates (i.e., questions or distractors), which can introduce noise into the models without necessarily enhancing their understanding of the deeper semantic relationships between question-answer and related distractors. This paper introduces a novel distractor generation model based on contrastive learning to train the model to recognize essential semantic features necessary to generate in-context distractors. The extensive experiments on two public datasets indicate that contrastive learning introduces a strong baseline model to the distractor generation task. It significantly outperforms recent models, increasing the NDCG@3 score from 24.68 to 32.33 on the MCQ dataset and from 26.66 to 36.68 on the SciQ dataset.

Fine-Tuning Encoder-Decoder Models with Contrastive Learning for In-Context Distractor Generation

As NLP models become increasingly integrated into real-world applications, it becomes clear that there is a need to address the fact that models often rely on and generate conflicting information. Conflicts could reflect the complexity of situations, changes that need to be explained and dealt with, difficulties in data annotation, and mistakes in generated outputs. In all cases, disregarding the conflicts in data could result in undesired behaviors of models and undermine NLP models’ reliability and trustworthiness. This survey categorizes these conflicts into three key areas: (1) natural texts on the web, where factual inconsistencies, subjective biases, and multiple perspectives introduce contradictions; (2) human-annotated data, where annotator disagreements, mistakes, and societal biases impact model training; and (3) model interactions, where hallucinations and knowledge conflicts emerge during deployment. While prior work has addressed some of these conflicts in isolation, we unify them under the broader concept of conflicting information, analyze their implications, and discuss mitigation strategies. We highlight key challenges and future directions for developing conflict-aware NLP systems that can reason over and reconcile conflicting information more effectively.

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