Morocco

This paper delves into the factors that contribute to the difficulty of problems for large language models (LLMs). We begin with a pilot test evaluating LLMs&#39; understanding of esoteric programming languages and find that LLMs struggle significantly when programs execute in an order that is unaligned with how the program is presented. This phenomenon leads to the hypothesis that LLM performance on reasoning correlates with the alignment between the order in which information is presented and the order in which it should be utilized. We demonstrate that this hypothesis holds broadly in mathematical reasoning: restructuring problems to align the order of information presentation with the order of utilization consistently improves performance across state-of-the-art models. We conjecture this occurs because LLMs acquire a strong tendency to verbalize information in presentation order during training on human text, a tendency detrimental in reasoning domains where the optimal utilization order often diverges from the presentation order. To provide further evidence, we construct pseudo-mathematical problems with nonsensical terms and quantify the verbalization flexibility of LLMs without interference from mathematical knowledge. Across twelve representative LLMs, we find that this flexibility exhibits a strong correlation (p = 0.87) with general reasoning performance rankings on LMArena.

EACL 2026

The Curse of Verbalization: How Presentation Order Constrains LLM Reasoning

llms

math

reasoning

## 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*





<br>

EMNLP 2025

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

This paper delves into the factors that contribute to the difficulty of problems for large language models (LLMs). We begin with a pilot test evaluating LLMs' understanding of esoteric programming languages and find that LLMs struggle significantly when programs execute in an order that is unaligned with how the program is presented. This phenomenon leads to the hypothesis that LLM performance on reasoning correlates with the alignment between the order in which information is presented and the order in which it should be utilized. We demonstrate that this hypothesis holds broadly in mathematical reasoning: restructuring problems to align the order of information presentation with the order of utilization consistently improves performance across state-of-the-art models. We conjecture this occurs because LLMs acquire a strong tendency to verbalize information in presentation order during training on human text, a tendency detrimental in reasoning domains where the optimal utilization order often diverges from the presentation order. To provide further evidence, we construct pseudo-mathematical problems with nonsensical terms and quantify the verbalization flexibility of LLMs without interference from mathematical knowledge. Across twelve representative LLMs, we find that this flexibility exhibits a strong correlation (p = 0.87) with general reasoning performance rankings on LMArena.

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) have shown strong performance on clinical text de-identification, the task of identifying sensitive text to protect patient privacy. However, their high computational requirements make them impractical for most clinical institutions. Furthermore, previous work has not examined their generalizability between formats, cultures, and genders. In this work, we systematically evaluate de-identification in fine-tuned transformer models (BERT, ClinicalBERT, ModernBERT), small LLMs (Llama 1-8B, Qwen 1.5-7B), and large LLMs (Llama-70B, Qwen-72B). We show that smaller models achieve comparable performance while substantially reducing inference cost, making them more practical for deployment. Moreover, we demonstrate that smaller models can be fine-tuned with limited data to outperform larger models in cross-culture de-identification. Our evaluation spans identifiers drawn from Mandarin, Hindi, Spanish, French, Bengali, and regional variations of English, in addition to gendered names. To improve robustness in multi-cultural contexts, we introduce and publicly release BERT-CrossCulture-DEID, a set of de-identification models based on BERT, ClinicalBERT, and ModernBERT, fine-tuned on MIMIC with identifiers from multiple cultures. Our findings provide the first comprehensive quantification of the efficiency-generalizability trade-off in de-identification and establish practical pathways for fair, efficient, and culturally inclusive clinical de-identification.

Towards Fair and Efficient De-identification: Quantifying the Efficiency and Generalizability of De-identification Approaches

Parameter-efficient fine-tuning (PEFT) adapts large pre-trained models by updating only a small subset of parameters. Recently, Representation Fine-Tuning (ReFT) has emerged as an effective alternative. ReFT shifts the fine-tuning paradigm from updating model weights to directly manipulating hidden representations that capture rich semantic information, and outperform state-of-the-art PEFTs in standalone settings. However, its application in Federated Learning (FL) remains challenging due to heterogeneity in clients' data distributions, model capacities, and computational resources. To address these challenges, we introduce Federated Representation Fine-Tuning (FedReFT), a novel approach to fine-tune clients' hidden representations. FedReFT applies sparse intervention layers to steer hidden representations directly, offering a lightweight and semantically rich fine-tuning alternative ideal for edge devices. However, representation-level updates are especially vulnerable to aggregation mismatch under different task heterogeneity, where naive averaging can corrupt semantic alignment. To mitigate this issue, we propose All-But-Me (ABM) aggregation, where each client receives the aggregated updates of others and partially incorporates them, enabling stable and personalized learning by balancing local focus with global knowledge. We further design an adaptive update strategy inspired by Test-Time Computing (TTC) to balance local and global contributions under heterogeneous conditions. FedReFT achieves state-of-the-art performance on commonsense reasoning, arithmetic reasoning, and GLUE benchmarks, while delivering 1xвЂ“49x higher parameter efficiency compared to leading LoRA-based methods.

FedReFT: Federated Representation Fine-Tuning with All-But-Me Aggregation

Despite notable advancements in prompting methods for Large Language Models (LLMs), such as Chain-of-Thought (CoT), existing strategies still suffer from excessive token usage and limited generalisability across diverse reasoning tasks. To address these limitations, we propose an Adaptive Causal Prompting with Sketch-of-Thought (ACPS) framework, which leverages structural causal models to infer the causal effect of a query on its answer and adaptively select an appropriate intervention (i.e., standard front-door and conditional front-door adjustments). This design enables generalisable causal reasoning across heterogeneous tasks without task-specific retraining. By replacing verbose CoT with concise Sketch-of-Thought, ACPS enables efficient reasoning that significantly reduces token usage and inference cost. Extensive experiments on multiple reasoning benchmarks and LLMs demonstrate that ACPS consistently outperforms existing prompting baselines in terms of accuracy, robustness, and computational efficiency.

Debiasing Large Language Models via Adaptive Causal Prompting with Sketch-of-Thought

Schema linking---the process of aligning natural language questions with database schema elements---is a critical yet underexplored component of Text-to-SQL systems. While recent methods have focused primarily on improving SQL generation, they often neglect the retrieval of relevant schema elements, which can lead to hallucinations and execution failures. In this work, we propose a context-aware bidirectional schema retrieval framework that treats schema linking as a standalone problem. Our approach combines two complementary strategies: table-first retrieval followed by column selection, and column-first retrieval followed by table selection. It is further augmented with techniques such as question decomposition, keyword extraction, and keyphrase extraction. Through comprehensive evaluations on challenging benchmarks such as BIRD and Spider, we demonstrate that our method significantly improves schema recall while reducing false positives. Moreover, SQL generation using our retrieved schema consistently outperforms full-schema baselines and closely approaches oracle performance, all without requiring query refinement. Notably, our method narrows the performance gap between full and perfect schema settings by 50%. Our findings highlight schema linking as a powerful lever for enhancing Text-to-SQL accuracy and efficiency.

Rethinking Schema Linking: A Context-Aware Bidirectional Retrieval Approach for Text-to-SQL

With the widespread adoption of Large Language Models (LLMs), concerns about potential misuse have emerged. To this end, watermarking has been adapted to LLM, enabling a simple and effective way to detect and monitor generated text. However, while the existing methods can differentiate between watermarked and unwatermarked text with high accuracy, they often face a trade-off between the quality of the generated text and the effectiveness of the watermarking process. In this work, we present a novel type of LLM watermark, Sparse WatermARK (or SpARK), which aims to mitigate this trade-off by applying watermarks to a small subset of generated tokens distributed across the text. To demonstrate this type of watermark, we introduce two novel variants, SpARK-P and SpARK-R, which achieve sparsity by anchoring watermarked tokens to words that have specific Part-of-Speech (POS) tags and specific hash values w.r.t a pseudorandom hash function, respectively. Our experimental results demonstrate that the proposed watermarking schemes, albeit embarrassingly simple, are incredibly effective, achieving high detectability while generating text that outperforms previous LLM watermarking methods in quality across various tasks. SpARK further advances the watermarking capability for LLMs while maintaining their generated text quality.

SpARK: An Embarrassingly Simple Sparse Watermarking in LLMs with Enhanced Text Quality

Large language models (LLMs) are increasingly used as knowledge discovery tools. Humanistic disciplines like historical linguistics and literary studies have shown interest in this capability. These fields often construct arguments on the basis of distinctions between phenomena like time-period or genre. Such methodological investments complicate reliance on LLMs pretrained over large sets of broadly-collected data. We show that efficient pretraining techniques produce useful models of semantic change over modest historical corpora without allowing potential contamination from anachronistic data. We verify that these trained-from-scratch models better respect historical divisions and are more computationally efficient compared to the standard approach of fine-tuning an existing LLM. We compare the trade-offs in general linguistic fluency versus detecting and characterizing various forms of linguistic change, and provide a pipeline implementation of our approach that can be readily adapted and applied to a wide range of diachronic phenomena.

Pretraining Language Models for Diachronic Linguistic Change Discovery

Code-switching, where speakers alternate between languages within a single utterance, poses unique challenges for language identification (LID). Existing LID models often fail to reliably identify English spoken with the accent of the matrix (dominant) language. We show that finetuning LID models with small amounts of such accented English significantly improves code-switched LID, without degrading performance on standard monolingual speech—a limitation observed with direct finetuning on code-switched utterances. This is achieved via low-rank adaptation (LoRA) on limited accented data, which allows models to adapt efficiently. To better evaluate performance, we introduce LangRank, a metric that captures the relative ranking of identified languages often overlooked by traditional metrics. Our method generalizes across multiple language pairs, including Hindi-English, Bengali-English, Mandarin-English, and Arabic-English, providing robust LID in code-switched multilingual contexts.

Improving Language Identification for Code-Switched Speech: The Pivotal Role of Accented English

Large Language Models (LLMs) have demonstrated remarkable capabilities, enabling language agents to excel at single-turn tasks. However, their application to complex, multi-step, and long-horizon tasks remains challenging. While reinforcement learning (RL) offers a promising avenue for addressing these challenges, mainstream approaches typically rely solely on sparse, outcome-based rewards вЂ” a limitation that becomes especially problematic for group-based RL algorithms lacking critic models, such as Group Relative Policy Optimization (GRPO). In such methods, uniformly rewarding or penalizing all actions within a trajectory can lead to training instability and suboptimal policies, because beneficial and detrimental actions are often entangled across multi-step interactions. To address this challenge, we propose SALT, a novel and lightweight framework that provides a finer-grained advantage assignment, derived solely from outcome rewards. We achieve this by constructing a graph from trajectories of the same prompt, which allows us to quantify the quality of each step and assign advantages accordingly. Crucially, SALT is designed as a plug-and-play module that seamlessly integrates with existing group-based RL algorithms вЂ” requiring no modifications to the rollout procedure and introducing negligible computational overhead. Extensive experiments on the WebShop, ALFWorld, and AppWorld benchmarks with various model sizes demonstrate that SALT consistently improves performance. We also conduct a thorough analysis to validate the design choices behind SALT and offer actionable insights.

SALT: Step-level Advantage Assignment for Long-horizon Agents via Trajectory Graph

Recent advancements in Large Language Models (LLMs) have enabled autonomous agents to decompose complex tasks, select appropriate tools, and execute structured workflows. However, a key challenge in this field is the lack of a universal, large-scale, and cross-domain benchmark to systematically evaluate LLMs' ability to reason over and utilize interconnected tools for automation. Existing benchmarks, such as TaskBench, focus on manually curated tool graphs for benchmark generation, which lack scalability and diversity across domains. To address this, we propose UniToolBench, a benchmark that incorporates automated tool graph construction by formulating link prediction as a probabilistic task, instead of relying on categorical LLM outputs. Furthermore, we introduce a confidence-based beam search sampling strategy to select high-confidence tool dependencies, ensuring more structured and semantically coherent subgraphs for evaluation. Through extensive experiments on multiple datasets, we demonstrate that while LLMs show promise in tool selection, significant challenges remain in parameter prediction and handling complex tool dependencies.

UniToolBench: A Benchmark for Tool-Augmented LLMs in Cross-Domain, Universal Task Automation

Predicting how audiences react to Arabic social media posts requires reasoning beyond textual sentiment: reactions emerge from collective interpretation moderated by engagement dynamics and topical context. We present a multi-task learning (MTL) framework that jointly learns (i) audience reaction classification (Love, Haha, Angry, Sad, Care, Wow), (ii) engagement magnitude regression (six reactions, comments, shares), and (iii) non-engagement detection. On a corpus of 158k Arabic Facebook posts spanning women's rights, gender debates, and economic empowerment, our model achieves a test macro-F1 of 72.4 and weighted-F1 of 89.1.

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Towards Fair and Efficient De-identification: Quantifying the Efficiency and Generalizability of De-identification Approaches

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