Morocco

Contemporary advancements in language model reasoning typically require computationally intensive reinforcement learning (RL) and massive datasets, creating barriers for resource-constrained teams. In this work, we demonstrate that high-quality, iterative training on minimal data can rival modern RL approaches. We introduce a resource-efficient framework that combines Direct Preference Optimization (DPO) and Supervised Fine-Tuning (SFT) with selective guidance from larger models, iteratively refining solutions through a &quot;reflect, rewrite, repeat&quot; cycle (R). Using Qwen-2.5-7B and Qwen 2.5-Math-7B as base models, our method shows meaningful performance improvements across arithmetic, symbolic and cognitive reasoning benchmarksвЂ”including GSM8K (83.1% в†’ 88.6%), AIME&#39;25@10 (20.0% в†’ 30.0%) and LastLetterConcat (40.7% в†’ 53.3%) problems. The model-agnostic nature of our R framework is further demonstrated through substantial improvements when applied to Mistral and LLaMA-based models. Remarkably, these gains are achieved using mere 700 basic arithmetic training samples, in stark contrast to the hundreds of thousands of examples typically required by RL-based systems. Our results suggest that reasoning improvements need not strictly depend on large-scale data. By emphasizing strategically curated training grounded in foundational principles, we achieve competitive generalization with minimal resource overhead. Our R pipeline also generates high-quality SFT data with high-fidelity reasoning traces as byproduct, further enabling scalable and annotation-free fine-tuning.

EACL 2026

Reflect, Rewrite, Repeat: How Simple Arithmetic Enables Advanced Reasoning in Small Language Models

small language modeling

efficient/low-resource methods for nlp

data efficiency

It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. <br>
[Full Message from General Chair Philipp Koehn](https://drive.google.com/file/d/1QiJFOmMjvDgsDo3i1XzlQnImxe3RU_Cy/view?usp=sharing) <br><br><br>
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ACL 2026

The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

Contemporary advancements in language model reasoning typically require computationally intensive reinforcement learning (RL) and massive datasets, creating barriers for resource-constrained teams. In this work, we demonstrate that high-quality, iterative training on minimal data can rival modern RL approaches. We introduce a resource-efficient framework that combines Direct Preference Optimization (DPO) and Supervised Fine-Tuning (SFT) with selective guidance from larger models, iteratively refining solutions through a "reflect, rewrite, repeat" cycle (R). Using Qwen-2.5-7B and Qwen 2.5-Math-7B as base models, our method shows meaningful performance improvements across arithmetic, symbolic and cognitive reasoning benchmarksвЂ”including GSM8K (83.1% в†’ 88.6%), AIME'25@10 (20.0% в†’ 30.0%) and LastLetterConcat (40.7% в†’ 53.3%) problems. The model-agnostic nature of our R framework is further demonstrated through substantial improvements when applied to Mistral and LLaMA-based models. Remarkably, these gains are achieved using mere 700 basic arithmetic training samples, in stark contrast to the hundreds of thousands of examples typically required by RL-based systems. Our results suggest that reasoning improvements need not strictly depend on large-scale data. By emphasizing strategically curated training grounded in foundational principles, we achieve competitive generalization with minimal resource overhead. Our R pipeline also generates high-quality SFT data with high-fidelity reasoning traces as byproduct, further enabling scalable and annotation-free fine-tuning.

technical paper

#### *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)*<br><br>

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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. 

Large Language Models (LLMs) often struggle with complex mathematical reasoning, where prose-based generation leads to unverified and arithmetically unsound solutions. Current prompting strategies like Chain of Thought still operate within this unreliable medium, lacking a mechanism for deterministic verification. To address these limitations, we introduce SymCode, a neurosymbolic framework that reframes mathematical problem-solving as a task of verifiable code generation using the SymPy library. We evaluate SymCode on challenging benchmarks, including MATH-500 and OlympiadBench, demonstrating significant accuracy improvements of up to 13.6 percentage points over baselines. Our analysis shows that SymCode is not only more token-efficient but also fundamentally shifts model failures from opaque logical fallacies towards transparent, programmatic errors. By grounding LLM reasoning in a deterministic symbolic engine, SymCode represents a key step towards more accurate and trustworthy AI in formal domains.

SymCode: A Neurosymbolic Approach to Mathematical Reasoning via Verifiable Code Generation

Distilling advanced Large Language Models' instruction-following capabilities into smaller models using a selected subset has become a mainstream approach in model training. While existing synthetic instruction data selection strategies rely mainly on single-dimensional signals (i.e., reward scores, model perplexity), they fail to capture the complexity of instruction-following across diverse fields. Therefore, we investigate more diverse signals to capture comprehensive instruction-response pair characteristics and propose three foundational metrics that leverage Multi-LLMs wisdom, informed by (1) diverse LLM responses and (2) reward model assessment. Building upon base metrics, we propose CROWDSELECT, an integrated metric incorporating a clustering-based approach to maintain response diversity. Our comprehensive experiments demonstrate that our foundation metrics consistently improve performance across 4 base models on MT-bench and Arena-Hard. CROWDSELECT, efficiently incorporating all metrics, achieves state-of-the-art performance in both Full and LoRA fine-tuning, showing improvements of 4.81% on Arena-Hard and 11.1% on MT-bench with Llama-3.2-3b-instruct. We hope our findings will bring valuable insights for future research in this direction.

CROWDSELECT: SyntheticInstruction Data Selection with Multi-LLM Wisdom

We conduct a systematic audit of three widely used reasoning benchmarks, SocialIQa, FauxPas-EAI, and ToMi, and uncover pervasive flaws in both benchmark items and evaluation methodology. Using five LLMs (GPT-{3, 3.5, 4, o1}, and LLaMA 3.1) as diagnostic tools, we identify structural, semantic, and pragmatic issues in benchmark design (e.g., duplicated items, ambiguous wording, and implausible answers), as well as scoring procedures that prioritize output form over reasoning process. Through systematic human annotation and re-evaluation on cleaned benchmark subsets, we find that model scores often improve not due to due to erratic surface wording variations and not to improved reasoning. Infact, further analyses show that model performance is highly sensitive to minor input variations such as context availability and phrasing, revealing that high scores may reflect alignment with format-specific cues rather than consistent inference based on the input. These findings challenge the validity of current benchmark-based claims about reasoning in LLMs, and highlight the need for evaluation protocols that assess reasoning as a process of drawing inference from available information, rather than as static output selection. We release audited data and evaluation tools to support more interpretable and diagnostic assessments of model reasoning.

Garbage In, Reasoning Out? Why Benchmark Scores are Unreliable and What to Do About It

Robustly evaluating the long-form storytelling capabilities of Large Language Models (LLMs) remains a significant challenge, as existing benchmarks often lack the necessary scale, diversity, or objective measures. To address this, we introduce WebNovelBench, a novel benchmark specifically designed for evaluating long-form novel generation. WebNovelBench leverages a large-scale dataset of over 4,000 Chinese web novels, framing evaluation as a synopsis-to-story generation task. We propose a multi-faceted framework encompassing eight narrative quality dimensions, assessed automatically via an LLM-as-Judge approach. Scores are aggregated using Principal Component Analysis and mapped to a percentile rank against human-authored works. Our experiments demonstrate that WebNovelBench effectively differentiates between human-written masterpieces, popular web novels, and LLM-generated content. We provide a comprehensive analysis of 24 state-of-the-art LLMs, ranking their storytelling abilities and offering insights for future development. This benchmark provides a scalable, replicable, and data-driven methodology for assessing and advancing LLM-driven narrative generation.

WebNovelBench: Placing LLM Novelists on the Web Novel Distribution

The rapid rise of deepfake technology poses a severe threat to social and political stability by enabling hyper-realistic synthetic media capable of manipulating public perception. However, existing detection methods struggle with two core limitations: (1) modality fragmentation, which leads to poor generalization across diverse and adversarial deepfake modalities; and (2) shallow inter-modal reasoning, resulting in limited detection of fine-grained semantic inconsistencies. To address these, we propose ConLLM (Contrastive Learning with Large Language Models), a hybrid framework for robust multimodal deepfake detection. ConLLM employs a two-stage architecture: stage 1 uses Pre-Trained Models (PTMs) to extract modality-specific embeddings; stage 2 aligns these embeddings via contrastive learning to mitigate modality fragmentation, and refines them using LLM-based reasoning to address shallow inter-modal reasoning by capturing semantic inconsistencies. ConLLM demonstrates strong performance across audio, video, and audio-visual modalities. It reduces audio deepfake EER by up to 50%, improves video accuracy by up to 8%, and achieves approximately 9% accuracy gains in audio-visual tasks. Ablation studies confirm that PTM-based embeddings contribute 9%вЂ“10% consistent improvements across modalities. Our code and data is available at: https://anonymous.4open.science/r/ConLLM-0D8A/README.md

Revealing the Truth with ConLLM for Detecting Multi-Modal Deepfakes

Large Language Model (LLM) safety is inherently pluralistic, reflecting variations in moral norms, cultural expectations, and demographic contexts. Yet, existing alignment datasets such as Anthropic-HH and DICES rely on demographically narrow annotator pools, overlooking variation in safety perception across communities. Demo-SafetyBench addresses this gap by modeling demographic pluralism directly at the prompt level, decoupling value framing from responses. In Stage I, prompts from DICES are reclassified into 14 safety domains (adapted from BeaverTails) using Mistral-7B-Instruct-v0.3, retaining demographic metadata and expanding low-resource domains via Llama-3.1-8B-Instruct with SimHash-based deduplication, yielding 43,050 samples. In Stage II, pluralistic sensitivity is evaluated using LLMs-as-Raters—Gemma-7B, GPT-4o, and LLaMA-2-7B—under zero-shot inference. Balanced thresholds (delta = 0.5, tau = 10) achieve high reliability (ICC = 0.87) and low demographic sensitivity (DS = 0.12), confirming that pluralistic safety evaluation can be both scalable and demographically robust. Code and data available at: https://github.com/usmaann/Demo-SafetyBench

Do Large Language Models Reflect Demographic Pluralism in Safety?

We design, implement, and evaluate adversarial decoding, a new, generic text generation technique that produces readable documents for adversarial objectives such as RAG poisoning, jailbreaking, and evasion of defensive filters. Prior generation methods either produce easily detectable gibberish (even methods that optimize for low perplexity), or cannot handle objectives that include embedding similarity. In particular, they cannot produce readable adversarial documents that (1) are retrieved by RAG systems in response to broad classes of queries, and (2) adversarially influence subsequent generation. We measure the effectiveness of adversarial decoding for different objectives and demonstrate that it outperforms existing methods while producing adversarial documents that cannot be automatically distinguished from natural documents by fluency and readability.

Adversarial Decoding: Generating Readable Documents for Adversarial Objectives

Large Language Models (LLMs) have been frequently used as automatic annotators for tasks such as Text Emotion Recognition (TER). We consider a scenario in which annotators assign at least one emotion label from a large set of options to a text snippet. For this emotion tagging task, we propose a novel zero-shot algorithm that leverages Best-Worst Scaling (BWS), prompting the LLM to choose the least and most suitable emotions for a given text from several label subsets. The LLM's choices can be represented by a graph linking labels via worse-than relations. Random walks on this graph yield the final score for each label. We compare our algorithm with naive prompting approaches as well as an established BWS-based method. Extensive experiments demonstrate the suitability of the method. It proves to compare favorably to the benchmarks in terms of both accuracy and calibration with respect to human annotations. Moreover, our algorithm's automatic annotations are shown to be suitable for finetuning lightweight emotion classification models. The proposed method consumes considerably fewer computational resources than the established BWS approach.

Training-Free Text Emotion Tagging via LLM-Based Best-Worst Scaling

Text-to-SQL systems translate natural language questions into executable SQL queries, and recent progress with large language models (LLMs) has driven substantial improvements in this task. Schema linking remains a critical component in Text-to-SQL systems, reducing prompt size for models with narrow context windows and sharpening model focus even when the entire schema fits. We present a zero-shot, training-free schema linking approach that first constructs a schema graph based on foreign key relations, then uses a single prompt to a lightweight LLM to extract source and destination tables from the user query, followed by applying classical path-finding algorithms and post-processing to identify the optimal sequence of tables and columns that should be joined, enabling the LLM to generate more accurate SQL queries. To handle real-world databases where foreign keys may be missing or inconsistent, we further propose an LLM-guided joinability discovery step that infers table connections before graph construction, ensuring robustness across diverse schemas. Despite being simple, cost-effective, and highly scalable, our method achieves state-of-the-art results on both the BIRD and Spider 2.0 benchmarks, outperforming previous specialized, fine-tuned, and complex multi-step LLM-based approaches.

SchemaGraphSQL: Efficient Schema Linking with Pathfinding Graph Algorithms for Text-to-SQL on Large-Scale Databases

Although expressive TTS systems aim to capture human-like emotion, little is known about how well emotional signals in text correspond to those in speech. In this short paper, we investigate the emotion alignment (Valence, Arousal, Dominance) between text and speech models. We use 8 large language models for identifying text emotions and two audio models for speech analysis, across three genres: Podcasts, Audiobooks and TED talks. Findings show that while language models perform well on emotion recognition from situational text, and the audio models performs well on speech, they show alignment on the Valence dimension only. Further, the genre of the content significantly impacts the alignment performance: audiobooks exhibit better text-audio correlation than TED talks. Our results highlight the limitations of text-based emotion expression in TTS systems: performance depends on genre, is mostly limited to Valence, and more context fails to improve emotional alignment between text and speech.

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