Morocco

Lexical semantics is concerned with both the multiple senses a word can adopt in different contexts, and the semantic relations that exist between meanings of different words. To investigate them, Contextualized Language Models are a valuable tool that provides context-sensitive representations that can be used to investigate lexical meaning. Recent works like XL-LEXEME have leveraged the task of Word-in-Context to fine-tune them to get more semantically accurate representations, but Word-in-Context only compares occurrences of the same lemma, limiting the range of captured information. In this paper, we propose an extension, Concept Differentiation, to include inter-words scenarios. We provide a dataset for this task, derived from SemCor data. Then we fine-tune several representation models on this dataset. We call these models Concept-Aligned Embeddings (CALE). By challenging our models and other models on various lexical semantic tasks, we demonstrate that the proposed models provide efficient multi-purpose representations of lexical meaning that reach best performances in our experiments. We also show that CALE&#39;s fine-tuning brings valuable changes to the spatial organization of embeddings.

EACL 2026 Main Conference

CALE : Concept-Aligned Embeddings for Both Within-Lemma and Inter-Lemma Sense Differentiation

synonymy

polysemy

lexical semantics

word embeddings

Lexical semantics is concerned with both the multiple senses a word can adopt in different contexts, and the semantic relations that exist between meanings of different words. To investigate them, Contextualized Language Models are a valuable tool that provides context-sensitive representations that can be used to investigate lexical meaning. Recent works like XL-LEXEME have leveraged the task of Word-in-Context to fine-tune them to get more semantically accurate representations, but Word-in-Context only compares occurrences of the same lemma, limiting the range of captured information. In this paper, we propose an extension, Concept Differentiation, to include inter-words scenarios. We provide a dataset for this task, derived from SemCor data. Then we fine-tune several representation models on this dataset. We call these models Concept-Aligned Embeddings (CALE). By challenging our models and other models on various lexical semantic tasks, we demonstrate that the proposed models provide efficient multi-purpose representations of lexical meaning that reach best performances in our experiments. We also show that CALE's fine-tuning brings valuable changes to the spatial organization of embeddings.

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. 

Mental models can play a critical role in assessing students' conceptual understanding of a topic. They not only offer insights into what students know but also into how effectively they can apply, relate to, and integrate concepts across various contexts. Thus, students' responses are critical markers of the quality of their understanding and not entities that should be merely graded. However, inferring mental models from student answers is challenging as it requires deep reasoning skills. We propose MMGrader, an approach that infers the quality of students' mental models from their multimodal responses using concept graphs as an analytical framework. In our evaluation with 9 openly available models, we found that the best-performing models fall short of human-level performance. This is because they only achieved an accuracy of approximately 40%, a prediction error of 1.1 units, and a scoring distribution fairly aligned with human scoring patterns. With improved accuracy, these can be highly effective assistants to teachers in inferring the mental models of their entire classrooms, enabling them to do so efficiently and help improve their pedagogies more effectively by designing targeted help sessions and lectures that strengthen areas where students collectively demonstrate lower proficiency.

How effective are VLMs in assisting humans in inferring the quality of mental models from Multimodal short answers?

Reliably ensuring Large Language Models (LLMs) follow complex instructions is a critical challenge, as existing benchmarks often fail to reflect real-world use or isolate compliance from task success. We introduce a modular framework that uses a dynamically generated dataset with up to 20 application-oriented generation constraints to enable a granular and independent analysis of this capability. Our evaluation of five LLMs from different families based on this new benchmark demonstrates that compliance is not a monolithic capability but varies significantly with constraint type, quantity, and position. The analysis reveals model-specific weaknesses, uncovers synergistic and conflicting interactions between instructions, and identifies distinct positional biases such as primacy and recency effects. These granular insights are critical for diagnosing model failures and developing more reliable LLMs for systems that demand strict adherence to complex instructions.

Deconstructing Instruction-Following: A New Benchmark for Granular Evaluation of Large Language Model Instruction Compliance Abilities

In many real-world applications, users rely on natural language instructions to guide large language models (LLMs) across a wide range of tasks. These instructions are often complex, diverse, and subject to frequent change. However, LLMs do not always attend to these instructions reliably, and users lack simple mechanisms to emphasize their importance beyond modifying prompt wording or structure. To address this, we present an inference-time method that enables users to emphasize specific parts of their prompt by steering the model's attention toward them, aligning the model's perceived importance of different prompt tokens with user intent. Unlike prior approaches that are limited to static instructions, require significant offline profiling, or rely on fixed biases, we dynamically update the proportion of model attention given to the user-specified parts--ensuring improved instruction following without performance degradation. We demonstrate that our approach improves instruction following across a variety of tasks involving multiple instructions and generalizes across models of varying scales.

Spotlight Your Instructions: Instruction-following with Dynamic Attention Steering

Efficient long-context processing remains a crucial challenge for contemporary large language models (LLMs), especially in resource-constrained environments. Soft compression architectures promise to extend effective context length by replacing long token sequences with smaller sets of learned compressed tokens. Yet, the limits of compressibility -- and when compression begins to erase task-relevant content -- remain underexplored. In this paper, we define token overflow as a regime in which compressed representations no longer contain sufficient information to answer a given query, and propose a methodology to characterize and detect it. In the xRAG soft-compression setting, we find that query-agnostic saturation statistics reliably separate compressed from uncompressed token representations, providing a practical tool for identifying compressed tokens but showing limited overflow detection capability. Lightweight probing classifiers over both query and context xRAG representations detect overflow with 0.72 AUC-ROC on average on HotpotQA, SQuADv2, and TriviaQA datasets, demonstrating that incorporating query information improves detection performance. These results advance from query-independent diagnostics to query-aware detectors, enabling low-cost pre-LLM gating to mitigate compression-induced errors.

Detecting Overflow in Compressed Token Representations for Retrieval-Augmented Generation

Subliminal learning is a recently observed phenomenon in which a student model acquires behavioral traits from a teacher by fine-tuning on teacher-generated data that contains no explicit mention of those traits. This is surprising because the setup resembles hard knowledge distillation: the student only sees the teacher’s final tokens rather than soft probability targets. In this work, we provide a systematic empirical study of subliminal learning in language models, focusing on its generality, sensitivity to training conditions and system prompts, and the adequacy of token-level explanations as underlying mechanisms. We reproduce the subliminal learning pipeline across multiple model families, finding that the phenomenon is model-dependent and not general. We then characterize how transfer strength varies with dataset size, dataset diversity, and fine-tuning time. We also study the stability of transferred traits under multiple rounds of distillation as well as the transfer of multiple traits at once, where stronger traits tend to dominate. Finally, we test token-level accounts, using token entanglement as a representative hypothesis. We find that specific tokens do not standalone carry trait-specific information, suggesting that token-level explanations alone are incomplete. Together, our results delineate key boundary conditions for subliminal learning and motivate further work toward a mechanistic account of how traits are encoded in seemingly unrelated data. The code for our experiments is available at Github Repository: https://github.com/YagneshV/subliminal-learning-codebase.

Understanding Subliminal Learning: Generality, Sensitivity, and Token-Level Explanations

This research proposal describes a cross-disciplinary project aimed at developing Digital Twins (DTs) of Alzheimer's Disease (AD) using Language Models (LMs). By mimicking the functional deficits observed in individuals with AD, these DTs will serve as tools for early detection and understanding of disease progression. Several approaches to altering the LM will be explored, and the resulting effects on brain score — an evaluation of the correlation between brain activity and the LM's internal activations — will be studied. Detection models will be trained based on each approach; these models will be compared against themselves and the state-of-the-art.

Two converging lines of evidence motivate this work: LMs achieve high accuracy in classifying AD from speech transcripts, and their internal representations correlate significantly with human brain activity during language processing. If successful, this project could lead to significant advancements in the early detection and monitoring of AD, ultimately improving patient outcomes.

Thesis proposal: COGNILENS: Analyzing Cognitive Decline in Language Models for Alzheimer's Monitoring

This thesis argues that the currently widely used Natural Language Processing algorithms possibly have various limitations related to the properties of the texts they handle and produce. With the wide adoption of these tools in rapid progress, we must ask what these limitations are and what are the possible implications of integrating such tools into our daily lives.

As a testbed, we have chosen the task of Neural Machine Translation (NMT). Nevertheless, we aim for general insights and outcomes, applicable to current Large Language Models (LLMs). We ask whether the algorithms used in NMT have inherent inductive biases that are beneficial for most types of inputs but might harm the processing of untypical texts, thereby contributing to a cycle of monotonous, repetitive language -- whether generated by machines or humans.
To explore this hypothesis, we define a set of measures to quantify text diversity based on its statistical properties, like uniformity or rhythmicity of word-level surprisal, on multiple scales (sentence, discourse, language). We conduct a series of experiments to investigate whether NMT systems struggle with maintaining the diversity of such texts, potentially reducing the richness of the generated language, compared to human translators.

We further analyze potential origins of these limitations within existing training objectives and decoding strategies. Ultimately, our goal is to propose and validate alternative approaches (e.g., loss functions, decoding algorithms) that maintain the diversity and complexity of language and that allow for better global planning of the output generation, enabling the models to better reflect the ambiguities inherent in human communication.

Thesis proposal: Are We Losing Textual Diversity to Natural Language Processing?

Recent advancements in Large Language Models (LLMs) have notably enhanced task-oriented dialogue systems, particularly in Dialogue State Tracking (DST), owing to their generative capabilities and strong generalization. Although recent approaches such as LDST and FnCTOD significantly improved cross-domain DST performance via supervised fine-tuning (SFT), these methods typically require substantial amounts of domain-specific data. In this paper, we address this limitation by employing Group Relative Policy Optimization (GRPO) - a critic-free reinforcement learning method that efficiently guides LLMs toward improved DST accuracy even under low-resource conditions. Our results on established DST benchmarks, including MultiWOZ 2.1 and 2.4, demonstrate that the RL approach achieves superior performance to existing methods while using significantly reduced out-of-domain training data. In addition, we found out that models pretrained specifically for tool-use tasks can be a better starting point, especially on small scales.

Call, Reward, Repeat: Advancing Dialog State Tracking with GRPO and Function Calling

Detecting personally identifiable information (PII) in user queries is critical for ensuring privacy in question-answering systems. Current approaches mainly redact all PII, disregarding the fact that some of them may be contextually relevant to the user’s question, resulting in a degradation of response quality. Large language models (LLMs) might be able to help determine which PII are relevant, but due to their closed source nature and lack of privacy guarantees, they are unsuitable for sensitive data processing. To achieve privacy-preserving PII detection, we propose CAPID, a practical approach that fine-tunes a locally owned small language model (SLM) that filters sensitive information before it is passed to LLMs for QA. However, existing datasets do not capture the context-dependent relevance of PII needed to train such a model effectively. To fill this gap, we propose a synthetic data generation pipeline that leverages LLMs to produce a diverse, domain-rich dataset spanning multiple PII types and relevance levels. Using this dataset, we fine-tune an SLM to detect PII spans, classify their types, and estimate contextual relevance. Our experiments show that relevance-aware PII detection with a fine-tuned SLM substantially outperforms existing baselines in span, relevance and type accuracy while preserving significantly higher downstream utility under anonymization.

CAPID: Context-Aware PII Detection for Question-Answering Systems

Music is a universal cultural practice that influences emotion, ritual and creativity, and it is now represented in many digital modalities: audio recordings, symbolic encodings (MIDI, MusicXML, ABC), visual scores and lyrics. Multimodal Large Language Models (MLLMs) have the ambition to process "everything", including music, and therefore promise to support musical analysis, creation and education. Despite this promise, systematic methods for evaluating whether a MLLM understands music are missing. Existing music‑focused benchmarks are fragmented, largely single‑modality, Western‑centric, and often do not require actual perception of the musical content; methodological details such as prompt design and answer‑extraction are frequently omitted or not discussed, and some evaluations rely on proprietary LLMs, hindering reproducibility and raising concerns about test‑data leakage. To fill this gap, this dissertation proposes to design a musically multimodal benchmark built on a transparent, fully open evaluation pipeline. The benchmark will present closed‑question‑answer items across four musical modalities, employ carefully engineered distractor options to enforce genuine perceptual engagement, and follow rigorously documented prompt‑selection and answer‑extraction procedures. It will further incorporate culturally diverse musical material beyond the dominant Western canon. Guided by three research questions: (1) how to devise robust, reproducible evaluation procedures, (2) how current MLLMs perform across modalities, and (3) how model scores relate to human musical abilities; the benchmark will enable precise diagnosis of model limitations, inform the development of more musically aware AI systems, and provide a principled basis for assessing practical usefulness to musicians and other stakeholders in the creative industry.

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