Morocco

We present FeatEng, a novel benchmark designed to evaluate the ability of large language models (LLMs) to perform feature engineering, a critical and knowledge-intensive task in data science. FeatEng assesses LLMs by their capacity to generate Python code that transforms raw tabular data into features that improve the performance of a downstream machine learning model. Our analysis of LLM outputs reveals that success on FeatEng often requires the application of significant world and domain knowledge, along with complex reasoning, to construct novel data representations. While focused on feature engineering, the benchmark probes a confluence of abilities indicative of an LLM&#39;s broader potential for practical, data-centric problem-solving. We demonstrate that FeatEng offers a targeted and efficient approach to assess a specific but crucial aspect of LLM capabilities relevant to real-world data science applications.

EACL 2026 Main Conference

Can Models Help Us Create Better Models? Evaluating LLMs as Data Scientists

We present FeatEng, a novel benchmark designed to evaluate the ability of large language models (LLMs) to perform feature engineering, a critical and knowledge-intensive task in data science. FeatEng assesses LLMs by their capacity to generate Python code that transforms raw tabular data into features that improve the performance of a downstream machine learning model. Our analysis of LLM outputs reveals that success on FeatEng often requires the application of significant world and domain knowledge, along with complex reasoning, to construct novel data representations. While focused on feature engineering, the benchmark probes a confluence of abilities indicative of an LLM's broader potential for practical, data-centric problem-solving. We demonstrate that FeatEng offers a targeted and efficient approach to assess a specific but crucial aspect of LLM capabilities relevant to real-world data science applications.

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. 

Recent advancements in large language models (LLMs) have shown remarkable potential in automating machine learning tasks. However, existing LLM-based agents often struggle with low diversity and suboptimal code generation. While recent work~\cite{chi2024sela} has introduced Monte Carlo Tree Search (MCTS) to address these issues, limitations persist in the quality and diversity of thoughts generated, as well as in the scalar value feedback mechanisms used for node selection. In this study, we introduce Introspective Monte Carlo Tree Search (\textbf{\textit{I-MCTS}}), a novel approach that iteratively expands tree nodes through an introspective process that meticulously analyzes solutions and results from parent and sibling nodes. This facilitates a continuous refinement of the node in the search tree, thereby enhancing the overall decision-making process. Furthermore, we integrate a Large Language Model (LLM)-based value model to facilitate direct evaluation of each node's solution prior to conducting comprehensive computational rollouts. A hybrid rewarding mechanism is implemented to seamlessly transition the Q-value from estimated score to actual performance scores. Applied to the various ML tasks, our approach demonstrates a 6% absolute improvement in performance compared to the strong open-source AutoML agents, showcasing its effectiveness in enhancing agentic AutoML systems. Resource available at \url{https://anonymous.4open.science/r/I-MCTS}

I-MCTS: Enhancing Agentic AutoML via Introspective Monte Carlo Tree Search

The current state of event detection research has two notable re-occurring limitations that we investigate in this study. First, the unidirectional nature of decoder-only LLMs presents a fundamental architectural bottleneck for natural language understanding tasks that depend on rich, bidirectional context. Second, we confront the conventional reliance on Micro-F1 scores in event detection literature, which systematically inflates performance by favoring majority classes. Instead, we focus on Macro-F1 as a more representative measure of a model's ability across the long-tail of event types. Our experiments demonstrate that models enhanced with sentence context achieve superior performance over canonical decoder-only baselines. Using Low-Rank Adaptation (LoRA) during finetuning provides a substantial boost in Macro-F1 scores in particular, especially for the decoder-only models, showing that LoRA can be an effective tool to enhance LLMs' performance on long-tailed event classes.

Event Detection with a Context-Aware Encoder and LoRA for Improved Performance on Long-Tailed Classes

Restoring power distribution networks after disruptions demands rapid, reliable coordination across repair crews, mobile power sources, and switching actions under strict constraints. Classical optimization yields high-quality plans but can be slow, while reinforcement learning often requires feeder-specific training and careful reward shaping. We recast restoration as language-conditioned planning: a large language model generates high-level restoration plans over a compact pre-validated catalogue of feasible actions. This constrained generation design makes decisions reliably, scalably, and interpretably, and allows for real-time human-in-the-loop decision-making while requiring no topology-specific setup or retraining. Our method achieves near-mixed-integer-linear programming performance on the IEEE 13-node standard power distribution feeder and outperforms a time-capped MILP solver on the IEEE 33-node standard feeder by around 13\%, while using less than 1\% of its wall-clock runtime.

LARA: LLM-based Agile Power Distribution Network Restoration from Disastrous Events

Large Language Models (LLMs) are increasingly being considered for high-stakes decision-making, yet their application in statistical risk analysis remains largely underexplored. A central challenge in this domain is enabling LLMs to effectively leverage historical data. To address this, we propose novel methods for extracting key information from raw data and translating it into structured contextual input within the LLM prompt. Applying our methods to a case study of power outage risk assessment, we demonstrate that this contextualization strategy significantly improves the LLM's performance in risk assessment tasks. While the LLM's numerical predictions still do not match those of a standard machine learning model, the LLM-based approach offers distinct advantages in versatility and interpretability. These findings demonstrate a new paradigm for contextualizing data to support risk assessment.

How to Contextualize Empirical Data for Risk Analysis with LLMs: A Case Study of Power Outages

Interpretability in black-box dense retrievers remains a central challenge in Retrieval-Augmented Generation (RAG). Understanding how queries and documents semantically interact is critical for diagnosing retrieval behavior and improving model design. However, existing dense retrievers rely on static embeddings for both queries and documents, which obscures this bidirectional relationship. Post-hoc approaches such as re-rankers are computationally expensive, add inference latency, and still fail to reveal the underlying semantic alignment. To address these limitations, we propose Interpretable Modular Retrieval Neural Networks (IMRNNs), a lightweight framework that augments any dense retriever with dynamic, bidirectional modulation at inference time. IMRNNs employ two independent adapters: one conditions document embeddings on the current query, while the other refines the query embedding using corpus-level feedback from initially retrieved documents. This iterative modulation process enables the model to adapt representations dynamically and expose interpretable semantic dependencies between queries and documents. Empirically, IMRNNs not only enhance interpretability but also improve retrieval effectiveness. Across seven benchmark datasets, applying our method to standard dense retrievers yields average gains of +6.35% nDCG, +7.14% recall, and +7.04% MRR over state-of-the-art baselines. These results demonstrate that incorporating interpretability-driven modulation can both explain and enhance retrieval in RAG systems.

IMRNNs: An Efficient Method for Interpretable Dense Retrieval via Embedding Modulation

Automatic Speech Recognition (ASR) performance is heavily dependent on the availability of large-scale, high-quality datasets. For low-resource languages, existing open-source ASR datasets often suffer from insufficient quality and inconsistent annotation, hindering the development of robust models. To address these challenges, we propose a novel and generalizable data aggregation and preprocessing pipeline designed to construct high-quality ASR datasets from diverse, potentially noisy, open-source sources. Our pipeline incorporates rigorous processing steps to ensure data diversity, balance, and the inclusion of crucial features like word-level timestamps. We demonstrate the effectiveness of our methodology by applying it to Vietnamese, resulting in a unified, high-quality 500-hour dataset that provides a foundation for training and evaluating state-of-the-art Vietnamese ASR systems.

Vietnamese Automatic Speech Recognition: A Revisit

In modern healthcare, radiology plays a pivotal role in diagnosing and managing diseases. However, the complexity of medical imaging data and the variability in interpretation can lead to inconsistencies and a lack of patient-centered insight in radiology reports. To address this challenge, a novel multimodal prompt-driven report generation framework Rad-Flamingo was developed, that integrates diverse data modalities—such as medical images, and clinical notes—to produce comprehensive and context-aware radiology reports. Our framework leverages innovative prompt engineering techniques to guide vision-language models in generating relevant information, ensuring these generated reports are not only accurate but also understandable to individual patients. A key feature of our framework is its ability to provide patient-centric explanations, offering clear and personalized insights into diagnostic findings and their implications. Additionally, we also demonstrate a synthetic data generation pipeline, to append any existing benchmark datasets’ findings and impressions with patient-centric explanation. Experimental results demonstrate that this framework’s effectiveness in enhancing report quality, improving understandability, and could foster better patient-doctor communication. This approach represents a significant step towards human-centered medical AI systems.

Rad-Flamingo: A Multimodal Prompt driven Radiology Report Generation Framework with Patient-Centric Explanations

While Large Language Models (LLMs) show remarkable capabilities, their complex reasoning skills require deeper investigation. We introduce **RiddleBench**, a new benchmark of 1,737 challenging puzzles designed to test reasoning beyond simple pattern matching. Our evaluation of state-of-the-art models reveals significant limitations, including hallucination cascades (uncritically accepting flawed peer reasoning) and poor self-correction due to strong self-confirmation bias. We also find that model performance is fragile, degrading when faced with reordered constraints or irrelevant information. RiddleBench serves as a resource for diagnosing these issues and guiding the development of more robust LLMs.

RiddleBench: A New Generative Reasoning Benchmark for LLMs

Current multi-modal benchmarks primarily focus on facts within individual images. However, they overlook the associative relations among multiple images, which necessitate conducting commonsense reasoning grounded in associated knowledge at different granularities (i.e., image-level and entity-level) as well as the ability to perceive the order of images. Therefore, we propose a multi-image relational association task and a meticulously curated Multi-granularity Multi-image Relational Association (MMRA) benchmark, comprising 1,024 samples. To systematically evaluate current LVLMs, we establish a system of associative relations among images that contains 11 subtasks (e.g., UsageSimilarity, SubEvent, etc.) at two granularity levels (i.e., image-level and entity-level), based on relations in ConceptNet. Our experiments reveal that entity-level multi-image perception tasks pose greater challenges for LVLMs than image-level tasks. Moreover, LVLMs perform poorly on spatial-related tasks, indicating limited spatial awareness. Furthermore, we find that LVLMs exhibit weak image order perception capabilities, and we design a method to significantly improve this ability, demonstrating that most current LVLMs do not adequately consider image order perception during pre-training.

MMRA: A Benchmark for Evaluating Multi-Granularity and Multi-Image Relational Association Capabilities in Large Visual Language Models

Despite significant progress in image captioning, generating accurate and descriptive captions remains a long-standing challenge. In this study, we propose Attention-Guided Image Captioning (AGIC), which amplifies salient visual regions directly in the feature space to guide caption generation. We further introduce a hybrid decoding strategy that combines deterministic and probabilistic sampling to balance fluency and diversity. To evaluate AGIC, we conduct extensive experiments on the Flickr8k and Flickr30k datasets. The results\footnote{We plan to release the code and data.} show that AGIC matches or surpasses several state-of-the-art models while achieving faster inference. Moreover, AGIC demonstrates strong performance across multiple evaluation metrics, offering a scalable and interpretable solution for image captioning.

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