Morocco

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.

EACL 2026

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

nlp in resource-constrained settings

efficient

text-to-sql

### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
**[Continue reading...](https://drive.google.com/file/d/1jX-qGhqVSZZCIrAnJaz798pflu5Irrdn/view?usp=sharing)**

*- Colin Cherry, Google, NAACL 2025 General Chair* 

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NAACL 2025

Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

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. 

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.

Demystifying Mixed Outcomes of Self-Training: Pre-training Analyses on Non-Toy LLMs

Explaining why content is hateful using natural language is crucial for fostering transparency in automated content moderation systems. However, evaluating the quality of such explanations remains an open challenge. General-purpose reward models (RMs), commonly used for scoring natural language outputs, are typically optimized for broad notions of safety. We argue that this optimization penalizes situations where references to stereotypes or offensive content are essential for faithful hate speech explanations. To address this gap, we introduce SBIC-Explain, a human-validated dataset of 370,788 LLM generated NLEs for offensive content, spanning three levels of human-annotated contextual richness: Tier 1: text-only, Tier 2: + classification-aware, and Tier 3: + semantics-informed. We hypothesize that as human-annotated context increases, explanations should better reflect human preferences. Yet, we find that existing RMs systematically assign lower scores to more contextually rich (and often more offensive) explanations, revealing a misalignment between model preferences and explanatory fidelity for this context. We propose HARM (Hate-Aware Reward Model), a RM that integrates interpretable signals to better align reward scores with the needs of hate speech explanation. HARM outperforms general-purpose baselines, improving NLE pair-wise preference.

HARM: Learning Hate-Aware Reward Model for Evaluating Natural Language Explanations of Offensive Content

Length generalization is the ability of language models to maintain performance on inputs longer than those seen during pretraining. In this work, we introduce a simple yet powerful position encoding (PE) strategy, Random Float Sampling (RFS), that generalizes well to lengths unseen during pretraining or fine-tuning. In particular, instead of selecting position indices from a predefined discrete set, RFS uses randomly sampled continuous values, thereby avoiding out-of-distribution (OOD) issues on unseen lengths by exposing the model to diverse indices during training. Since assigning indices to tokens is a common and fundamental procedure in widely used PEs, the advantage of RFS can easily be incorporated into, for instance, the absolute sinusoidal encoding, RoPE, and ALiBi. Experiments corroborate its effectiveness by showing that RFS results in superior performance in length generalization tasks as well as zero-shot commonsense reasoning benchmarks.

Position Encoding with Random Float Sampling Enhances Length Generalization of Transformers

Argument Mining (AM) aims to identify and interpret argumentative structures in unstructured text, with Argument Component Classification (ACC) as a core task. Despite significant advances, most ACC approaches rely on manually pre-segmented inputs, an assumption that rarely holds in practice due to the high cost and effort of expert human annotation, creating a major bottleneck for scalable AM systems. In this work, we focus on the foundation Argument Component Segmentation (ACS) task by proposing a fine-grained, paired-tag annotation schema that explicitly distinguishes between relevant and surrounding content, thus overcoming the limitations of previous single-separator approaches. Leveraging small and open Large Language Models (LLMs) fine-tuned on our paired-tag annotation schema, we can perform ACS with quality comparable to human expert annotators across multiple benchmark datasets. We further validate our approach on the downstream ACC task, showing that automated segmentation with fine-tuned LLMs yields ACC performances comparable to pipelines relying on human annotations. These findings suggest that reliable automated ACS via LLMs is both feasible and effective, paving the way for more scalable AM pipelines without human intervention.

Argument Component Segmentation with Fine-Tuned Large Language Models

Deep neural network classifiers for dysarthria impairment severity face limitations regarding interpretability and treatment guidance. To overcome these, we introduce CLINIC-GENIE, an explainable two-stage framework consisting of: (1) CLINIC, a dysarthria severity classification model combining acoustic and speech embeddings with Clinically Explainable Acoustic Features (CEAFs); and (2) GENIE, a module translating CEAFs and their Shapley values into intuitive natural language explanations via a large language model. CLINIC achieved a balanced accuracy of 0.952 (17.3\% improvement over using CEAFs alone), and certified speech-language pathologists rated explanations from CLINIC-GENIE with an average fidelity score of 4.94, confirming enhanced clinical utility.

Diagnosis of Dysarthria Severity and Explanation Generation Using XAI-Enhanced CLINIC-GENIE on Diadochokinetic Tasks

The paper extends the Data Movement Distance (DMD) -- a metric defined to measure the locality in computer memory -- to text by defining a normalized version called nDMD. A key feature of nDMD is a new term designed to better characterize low-frequency tokens. By evaluating nDMD on English subset of the M4 dataset and GenAI detection shared task, the paper shows three key findings. First, nDMD is systematically higher in human-written text than in machine-generated text. Second, nDMD-based features not only outperform frequency baselines but also improve overall performance when combined. Finally, the proposed DMD normalization is more effective in distinguishing human and machine text than alternative approaches.

Ranking Human and LLM Texts Using Locality Statistics

The first pre-processing step for constructing web-crawled LLM pre-training datasets involves extracting text from HTML. Despite the immense diversity of web content, existing open-source datasets predominantly apply a single fixed extractor on all webpages. In this work, we investigate whether this practice leads to suboptimal dataset coverage. We first show that while different extractors yield similar performance on standard language understanding tasks, the pages surviving a fixed filtering pipeline can differ substantially. This suggests a simple intervention: by taking a Union over different extractors, we can increase the token yield of DCLM-Baseline by up to 71% while maintaining benchmark performance. We further show that for structured content such as tables and codeblocks, extractor choice significantly impacts downstream task performance, with differences up to 8 percentage points (p.p.) on WikiTQ and 2 p.p. on HumanEval.

Beyond a Single Extractor: Re-thinking HTML-to-Text Extraction for LLM Pre-training

The discourse around toxicity and LLMs in NLP largely revolves around detection tasks. This work shifts the focus to evaluating LLMs' _reasoning_ about toxicity&mdash;from their explanations that justify a stance&mdash;to enhance their trustworthiness in downstream tasks. Despite extensive research on explainability, it is not straightforward to adopt existing methods to evaluate free-form toxicity explanation due to their over-reliance on input text perturbations, among other challenges. To account for these, we propose a novel, theoretically-grounded multi-dimensional criterion (HAF), that measures the extent to which LLMs' free-form toxicity explanations align with those of a rational human under ideal conditions. We develop six metrics, based on uncertainty quantification, to comprehensively evaluate HAF of LLMs' toxicity explanations with no human involvement, and highlight how "non-ideal" the explanations are. We conduct several experiments on three Llama models (of size up to 70B) and an 8B Ministral model on five diverse toxicity datasets. Our results show that while LLMs generate plausible explanations to simple prompts, their reasoning about toxicity breaks down when prompted about the nuanced relations between the complete set of reasons, the individual reasons, and their toxicity stances, resulting in inconsistent and irrelevant responses. We open-source our [code](https://anonymous.4open.science/r/safte-7AE0/) and [LLM-generated explanations](https://anonymous.4open.science/r/safte-7AE0/haf_results/HAF_sample_data.xlsx).

Argument-Based Consistency in Toxicity Explanations of LLMs

Recent advances in large language models have enabled mental health dialogue systems, yet existing approaches remain predominantly reactive, lacking systematic user state modeling for proactive therapeutic exploration. We introduce PsyProbe, a dialogue system designed for the exploration phase of counseling that systematically tracks user psychological states through the PPPPPI framework (Presenting, Predisposing, Precipitating, Perpetuating, Protective, Impact) augmented with cognitive error detection. PsyProbe combines State Builder for extracting structured psychological profiles, Memory Construction for tracking information gaps, Strategy Planner for Motivational Interviewing behavioral codes, and Response Generator with Question Ideation and Critic/Revision modules to generate contextually appropriate, proactive questions. We evaluate PsyProbe with 27 participants in real-world Korean counseling scenarios, including automatic evaluation across ablation modes, user evaluation, and expert evaluation by a certified counselor. The full PsyProbe model consistently outperforms baseline and ablation modes in automatic evaluation. User evaluation demonstrates significantly increased engagement intention and improved naturalness compared to baseline. Expert evaluation shows that PsyProbe substantially improves core issue understanding and achieves question rates comparable to professional counselors, validating the effectiveness of systematic state modeling and proactive questioning for therapeutic exploration.

PsyProbe: Proactive and Interpretable Dialogue through User State Modeling for Exploratory Counseling

LLM agents have become increasingly sophisticated, especially in the realm of cybersecurity. Researchers have shown that LLM agents can exploit real-world vulnerabilities when given a description of the vulnerability and toy capture-the-flag problems. However, these agents still perform poorly on real-world vulnerabilities that are unknown to the agent ahead of time (zero-day vulnerabilities). In this work, we show that teams of LLM agents can exploit real-world, zero-day vulnerabilities. Prior agents struggle with exploring many different vulnerabilities and long-range planning when used alone. To resolve this, we introduce HPTSA, a system of agents with a planning agent that can launch subagents. The planning agent explores the system and determines which subagents to call, resolving long-term planning issues when trying different vulnerabilities. We construct a benchmark of 14 real-world vulnerabilities and show that our team of agents improve over prior agent frameworks by up to 4.3X.

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Demystifying Mixed Outcomes of Self-Training: Pre-training Analyses on Non-Toy LLMs

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