Morocco

Continual pre-training (CPT) has been widely adopted as a method for domain expansion in large language models. However, CPT has consistently been accompanied by challenges, such as the difficulty of acquiring large-scale domain-specific datasets and high computational costs. In this study, we propose a novel method called Test-Enhanced Learning for Language Model Enrichment (TELLME) to alleviate these issues. TELLME leverages the Test-Enhanced Learning (TEL) principle, whereby the model&#39;s learning efficiency is improved using quizzes during training. It integrates this principle with CPT, thereby promoting efficient domain-specific knowledge acquisition and long-term memory retention. Experimental results demonstrate that TELLME outperforms existing methods by up to 23.6% in the financial domain and achieves a 9.8% improvement in long-term memory retention.

EACL 2026

TELLME: Test-Enhanced Learning for Language Model Enrichment

test-enhanced learning

domain-specific tuning

domain-specific llms

continual pre-training

continual learning

## 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.

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EMNLP 2022

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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.

Continual pre-training (CPT) has been widely adopted as a method for domain expansion in large language models. However, CPT has consistently been accompanied by challenges, such as the difficulty of acquiring large-scale domain-specific datasets and high computational costs. In this study, we propose a novel method called Test-Enhanced Learning for Language Model Enrichment (TELLME) to alleviate these issues. TELLME leverages the Test-Enhanced Learning (TEL) principle, whereby the model's learning efficiency is improved using quizzes during training. It integrates this principle with CPT, thereby promoting efficient domain-specific knowledge acquisition and long-term memory retention. Experimental results demonstrate that TELLME outperforms existing methods by up to 23.6% in the financial domain and achieves a 9.8% improvement in long-term memory retention.

poster

#### *Message from the General Chair, Aline Villavicencio*
I’m delighted and honoured to welcome you to the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2026), taking place in the beautiful city of Rabat, in Morocco, in March 24-29, 2026. EACL is the flagship European conference of the Association and EACL 2026 proudly continues our field’s tradition of excellence in scholarship, innovation, and inclusivity. I am deeply grateful to the many volunteers whose dedication, generosity, and tireless efforts have made this conference possible.
For the first time EACL is being hosted in the African continent. This is an important milestone for our community, and we are grateful to our Moroccan hosts for enabling this historic moment by bringing this edition of EACL to Rabat. We are also delighted that the Second Arabic NLP School is co-located with EACL. We hope attendees enjoy this wonderful opportunity to strengthen ties with the Computational Linguistics communities across the African continent. *[Read full message](https://drive.google.com/file/d/14NlmHvwM6fPJuMmOvVh7K0vtQbEyv3SZ/view?usp=sharing)* 

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Welcome to the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL). EACL 2026 will be held in Rabat, Morocco, from March 24–29, 2026. 

Semantic Textual Similarity (STS) is a central embedding task in natural language processing, yet its definition is inherently ambiguous and varies across datasets. While previous studies have contributed to improving the interpretability of embedding models, most of these advancements are method-based and research on the relationships among datasets remains limited. In this paper, we introduce a comparative framework that attaches lightweight poolers to a frozen encoder, enabling consistent analysis across STS, Paraphrase identification (PI), and Triplet datasets. Our experiments on 21 datasets show that semantic notions are highly correlated across STS/PI settings, whereas style is a distinct concept that requires explicit separation from semantics. Layer-wise and hierarchical clustering analyses further reveal the differences in the properties among concepts and the structural organization of the embedding representation space. These findings suggest that semantic and style should be treated as distinct components in embedding models, with implications for both interpretability and practical applications.

From Semantics to Style: A Cross-Dataset Comparative Framework for Sentence Similarity Predictions

We curate a 980,061-article corpus of climate-related financial news from the Dow Jones Newswire (2000вЂ“2023) and introduce a three-stage ActorвЂ“FrameвЂ“Argument (AFA) pipeline that uses large language models to extract actors, stances, frames, and argumentative structures. We conduct AFA extraction on a stratified, uncertainty-enriched sample of 4,143 articles that preserves the temporal and thematic distributions of the full corpus. Reliability is established with a 2,000-article human-annotated gold standard and a Decompositional Verification Framework (DVF) that decomposes evaluation into completeness, faithfulness, coherence, and relevance, with multi-judge scoring calibrated against human ratings. Our longitudinal analysis uncovers a structural shift after 2015: coverage transitions from risk and regulatory-burden frames toward economic opportunity and technological innovation; financial institutions and companies increasingly deploy opportunity-centered arguments, while NGOs emphasize environmental urgency and governments stress compliance. Methodologically, we provide a replicable paradigm for longitudinal media analysis with LLMs. For high-stake domain insights, we map how the financial sector has internalized and reframed the climate crisis across two decades.

Actors, Frames and Arguments: A Multi-Decade Computational Analysis of Climate Discourse in Financial News using Large Language Models

Large language models (LLMs) have shown remarkable progress in reasoning across multiple domains. However, it remains unclear whether their abilities reflect genuine reasoning or sophisticated pattern matching, a distinction critical in medical decision-making, which requires reliable multistep problem-solving. To this end, we conducted one of the largest evaluations of its kind, assessing 77 LLMs with diverse fine-tuning approaches, ranging from 1 billion parameters to frontier models. Guided by medical problem-solving theory, we selected three medical question-answering benchmarks targeting key reasoning skills: reasoning processes, susceptibility to cognitive biases, and metacognitive abilities. Additionally, we manually annotated a subset of questions to assess the abduction, deduction, and induction capabilities of LLMs, offering fine-grained insights into the reasoning processes employed by physicians, an aspect that has received relatively limited attention in this domain. Most models, particularly smaller ones, struggled even with specialized fine-tuning or advanced prompting. Larger models performed better but still showed clear limitations in complex medical reasoning, highlighting the need to improve specific reasoning strategies to better reflect physician decision-making. The datasets and code used in this study are publicly available at: https://anonymous.4open.science/r/Can-LLMs-Reason-Like-Doctors-6B20/README.md

Can LLMs Reason Like Doctors? Exploring the Limits of Large Language Models in Complex Medical Reasoning

We present a comprehensive approach for multiword expression (MWE) identification that combines binary token-level classification, linguistic feature integration, and data augmentation. Our DeBERTa-v3-large model achieves 69.8% F1 on the CoAM dataset, surpassing the best results (Qwen-72B, 57.8% F1) on this dataset by 12 points while using 165 times fewer parameters. We achieve this performance by (1) reformulating detection as binary token-level START/END/INSIDE classification rather than span-based prediction, (2) incorporating NP chunking and dependency features that help discontinuous and NOUN-type MWEs identification, and (3) applying oversampling that addresses severe class imbalance in the training data. These results demonstrate that carefully designed smaller models can substantially outperform LLMs on structured NLP tasks, with important implications for resource-constrained deployments.

Binary Token-Level Classification with DeBERTa for All-Type MWE Identification: A Lightweight Approach with Linguistic Enhancement

Evaluating spoken assistants requires judging \emph{content} (C), \emph{voice quality} (VQ), and \emph{paralinguistics} (EQ). Most benchmarks collapse these dimensions into a single overall preference. We show this creates artifacts (forced winners and untyped ties) that overweight textual content and make overall accuracy "hackable" by transcript-only judges. To address this, we re-annotate two public datasets using a Human Chain-of-Thought (HCoT) protocol that elicits \emph{dimension-first} judgments with \emph{typed ties} (\emph{both-good} vs. \emph{both-bad}). This yields reliable, diagnostic labels validated under a protocol aligned with ITU-T guidance on separate dimension ratings. Building on HCoT, we propose \emph{JSON-Judge}, a training-free, two-stage evaluator: (i) assemble a compact JSON blueprint of inexpensive, off-the-shelf signals for C/VQ/EQ; (ii) prompt an LLM for per-dimension decisions, which a deterministic rule maps to the overall label. Across SpeakBench and S2S-Arena, JSON-Judge attains higher overall agreement with HCoT than transcript-only and audio-LLM judges and delivers stronger EQ fidelity, while being \emph{significantly cheaper than audio-LLM judges}. We will release the HCoT re-annotations and the JSON blueprints/prompts, providing a scalable, interpretable, and human-centric framework for evaluating voice assistants.

Hearing Between the Lines: Unlocking the Reasoning Power of LLMs for Speech Evaluation

Recent advances in audio generation led to an increasing number of deepfakes, making the general public more vulnerable to financial scams, identity theft, and misinformation. Audio deepfake detectors promise to alleviate this issue, with many recent studies reporting accuracy rates close to 99%. However, these methods are typically tested in an in-domain setup, where the deepfake samples from the training and test sets are produced by the same generative models. To this end, we introduce XMAD-Bench, a large-scale cross-domain multilingual audio deepfake benchmark comprising 668.8 hours of real and deepfake speech. In our novel dataset, the speakers, the generative methods, and the real audio sources are distinct across training and test splits. This leads to a challenging cross-domain evaluation setup, where audio deepfake detectors can be tested "in the wild". Our in-domain and cross-domain experiments indicate a clear disparity between the in-domain performance of deepfake detectors, which is usually as high as 100%, and the cross-domain performance of the same models, which is sometimes similar to random chance. Our benchmark highlights the need for the development of robust audio deepfake detectors, which maintain their generalization capacity across different languages, speakers, generative methods, and data sources. Our benchmark is publicly released at https://anonymous.4open.science/r/XMAD-Bench-detection/.

XMAD-Bench: Cross-Domain Multilingual Audio Deepfake Benchmark

KV caching significantly improves the efficiency of Large Language Model (LLM) inference by storing attention states from previously processed tokens, enabling faster generation of subsequent tokens. However, as sequence length increases, the KV cache quickly becomes a major memory bottleneck. To address this, we propose PagedEviction, a novel fine-grained, structured KV cache pruning strategy that enhances the memory efficiency of vLLM's PagedAttention. Unlike existing approaches that rely on attention-based token importance or evict tokens across different vLLM pages, PagedEviction introduces an efficient block-wise eviction algorithm tailored for paged memory layouts. Our method integrates seamlessly with PagedAttention without requiring any modifications to its CUDA attention kernels. We evaluate PagedEviction across Llama-3.1-8B-Instruct, Llama-3.2-1B-Instruct, and Llama-3.2-3B-Instruct models on the LongBench benchmark suite, demonstrating improved memory usage with better accuracy than baselines on long context tasks.

PagedEviction: Structured Block-wise KV Cache Pruning for Efficient Large Language Model Inference

Diffusion language models (DLMs) have recently emerged as a compelling alternative to autoregressive generation, offering parallel generation and improved global coherence. During inference, DLMs generate text by iteratively denoising masked sequences in parallel; however, determining which positions to unmask and which tokens to commit forms a large combinatorial search problem. Existing inference methods approximate this search using heuristics, which often yield suboptimal decoding paths; other approaches instead rely on additional training to guide token selection. To introduce a principled search mechanism for DLMs inference, we introduce MEDAL, a framework that integrates Monte Carlo Tree SEarch initialization for Diffusion LAnguage Model inference. We employ Monte Carlo Tree Search at the initialization stage to explore promising unmasking trajectories, providing a robust starting point for subsequent refinement. This integration is enabled by restricting the search space to high-confidence actions and prioritizing token choices that improve model confidence over remaining masked positions. Across multiple benchmarks, MEDAL achieves up to 22.0\% improvement over existing inference strategies, establishing a new paradigm for search-based inference in diffusion language models.

Diffusion Language Model Inference with Monte Carlo Tree Search

The Text-to-SQL task translates natural language questions into SQL queries, enabling intuitive database interaction for non-experts. While recent methods leveraging Large Language Models (LLMs) achieve strong performance, their reliance on proprietary models raise concerns about deployment feasibility and data privacy. In this work, we introduce LitE-SQL, a Lightweight and Efficient framework with two components: (i) a Schema Retriever that performs efficient schema linking using a vector database of pre-computed schema embeddings, and (ii) a SQL Generator fine-tuned in two stagesвЂ”supervised fine-tuning followed by execution-guided reinforcementвЂ”enabling self-correction without costly multi-candidate generation. On BIRD, LitE-SQL achieves 72.10% execution accuracy, and on Spider 1.0 it reaches 88.45%, demonstrating comparable or superior performance to LLM-based methods despite using 2Г— to 30Г— fewer parameters. Our findings demonstrate that high-quality Text-to-SQL generation is feasible with lightweight models, offering a practical solution for privacy-sensitive and resource-constrained settings.

LitE-SQL: A Lightweight and Efficient Text-to-SQL Framework with Vector-based Schema Linking and Execution-Guided Self-Correction

We investigate self-training of large language models (LLMs), in which models are recursively trained on their own generated text. Prior studies have reported conflicting outcomes in this setting: some find evidence of performance gains (i.e., self-improvement), while others observe performance degradation (i.e., model collapse). To clarify this discrepancy, we use the OLMo-2 models as non-toy LLMs and perform multiple rounds of continual pre-training using self-generated text with varying data synthesis and filtering strategies. Our experiments show that naive self-training does not improve either perplexity on the original pre-training corpus nor downstream task performance, regardless of model size. These results suggest that model collapse observed in naive self-training is inherent to the training procedure, while self-improvement likely owes its success not to the model’s autonomous refinement but to human designed, strategic synthetic pipelines that inject external intelligence.

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From Semantics to Style: A Cross-Dataset Comparative Framework for Sentence Similarity Predictions

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