Morocco

Recent advances in Sparse Autoencoders (SAEs) have revealed interpretable features within large language models (LLMs), including features that are specific to individual languages.
In prior work, these features have been used to steer a model’s output language.
However, the impact of SAE-based language steering on output quality and task performance, as well as its relationship to simpler prompting-based approaches, remains unclear.
In this work, we study the effects of language steering using SAE features across multiple tasks and models.
We apply language-specific SAE feature steering to three LLMs from two model families and evaluate it on a translation task and a multilingual question-answering task.
We compare SAE-based steering against prompting and language neuron-based steering, and examine a combined prompting-and-steering approach.
On the translation task, SAE feature steering achieves an average target-language accuracy of 92\% across models and languages, consistently outperforming language neuron-based steering, but slightly underperforming prompting in language accuracy and output quality.
In contrast, on the multilingual question-answering task, SAE-based steering enables stronger language control than prompting, and combining steering with prompting yields the best overall language control and task performance.
These findings demonstrate the potential of SAE features as a tool for controllable multilingual generation.

EACL 2026

Evaluating the Impact of SAE-based Language Steering on LLM Performance

language steering

mechanistic interpretability

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair: 
*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s 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.* 
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ACL 2025

Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

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. 

Police interrogation transcripts are key evidential documents, yet their linguistic form is rarely systematically analyzed, despite directly shaping judicial interpretation. This study presents the first computational forensic linguistic profiling of Italian police transcripts, focusing on the two transcription formats used in practice: narrative monologues and question-answer (Q-A) transcripts. Using automated extraction of 147 linguistic features, we analyze 50 authentic transcripts against a multi-genre Italian reference corpus to support more transparent evaluation of police transcripts by clarifying how transcription formats systematically shape evidential interpretation in judicial contexts. Narrative monologues exhibit deeper syntactic embedding, higher past-tense usage, and more first-person singular verbs, supporting coherent and temporally ordered recounting of events. Q-A transcripts, by contrast, show longer subordinate chains, more clausal complements, and higher pronoun frequency, reflecting interactive turn-taking and procedural dynamics. Rather than aiming at predictive classification, the study reveals the linguistic mechanisms shaping transcription formats and demonstrates that structurally and legally informed features reliably distinguish them. Computational models reliably capture genre-specific cues, offering scalable, empirically grounded insights into transcription practices and evidential reliability.

A Computational Forensic Linguistic Analysis of Narrative and Question-Answer Structures in Italian Police Interrogation Transcripts

The spread of misinformation has prompted extensive research on machine-learning–based fake news detection. However, existing datasets differ substantially in content distributions and annotation policies, complicating fair evaluation and generalization assessment. We refer to these structural differences as dataset bias. In this study, we quantitatively analyze dataset bias across multiple public fake news datasets (Kaggle, FNN, ISOT, and NELA-GT-2019/2020) with different annotation granularities, including article-level and publisher-level labels. Using document embedding–based similarity analysis and article category distributions, we examine how such biases affect detection performance under in-dataset and cross-dataset evaluation settings. Furthermore, to leverage large-scale but coarse-grained publisher-level data, we compare proxy-label training with a semi-supervised learning approach based on Virtual Adversarial Training (VAT). Our results show that detection performance strongly depends on dataset-specific biases, and that proxy-label training and SSL exhibit complementary, and sometimes opposite, strengths depending on whether the evaluation emphasizes in-dataset performance or cross-dataset generalization. These findings highlight the importance of appropriate training strategies and evaluation protocols when using heterogeneous fake news datasets.

Fake News Detection Strategies under Dataset Bias: Using Large-scale Coarse-grained Labels

Training a language model for low-resource languages is challenging due to data scarcity and computational cost. Tokenizer transfer offers a way to adapt a pre-trained model to a new tokenizer without full retraining, improving efficiency and cross-lingual applicability. To the best our of knowledge, we present the first controlled evaluation of tokenizer transfer on monolingually pretrained base models trained on language-specific corpora, Orthogonal Mapping Pursuit (OMP) and Fast Vocabulary Transfer (FVT), across six languages and multiple finetuning regimes. Using the Goldfish model family, we evaluate using byte-normalized log-perplexity and MultiBlimp accuracy for target-language adaptability, source-language retention, and the interaction between transfer and monolingual or mixed finetuning. OMP with monolingual target finetuning yields the best target-language scores (lower log-perplexity and higher MultiBlimp) among our evaluated conditions, compared with (i) a model trained only on the source language, (ii) a model trained on a smaller amount of target-language data, and (iii) the source language model adapted via standard finetuning on the target data. The results suggest tokenizer transfer is a compute-efficient alternative for low-resource LM training: train a monolingual tokenizer for the target language, transfer it to a larger pre-trained model, and fine-tune using the target data.

Efficient Low-Resource Language Models Using Tokenizer Transfer

Nested named entity recognition identifies entities contained within other entities, but requires expensive multi-level annotation. While flat NER corpora exist abundantly, nested resources remain scarce. We investigate whether models can learn nested structure from flat annotations alone, evaluating four approaches: string inclusions (substring matching), entity corruption (pseudo-nested data), flat neutralization (reducing false negative signal), and a hybrid fine-tuned + LLM pipeline. On NEREL, a Russian benchmark with 29 entity types where 21\% of entities are nested, our best combined method achieves 26.37\% inner F1, closing 40\% of the gap to full nested supervision. Code is available at https://github.com/fulstock/Learning-from-Flat-Annotations.

Learning Nested Named Entity Recognition from Flat Annotations

This paper investigates the capabilities of LLMs to detect and explain fine-grained emotional social influence techniques in textual dialogues, as well as human preferences for technique explanations. We present findings from our two studies. In Study 1, a dataset of 238 Polish dialogues is introduced, each annotated with detailed span-level labels. On this data, we evaluate the performance of LLMs on two tasks: detecting 11 emotional social influence techniques and identifying text spans corresponding to specific techniques. The results indicate that current LLMs demonstrate limited effectiveness in accurately detecting fine-grained emotional social influence.
In Study 2, we examine various LLM-generated explanations through human pairwise preferences and four criteria: comprehensibility, cognitive coherence, completeness, and soundness, with the latter two emerging as the most influential on general human preference. All data, including human annotations, are publicly available as the EmoSocInflu dataset (https://github.com/social-influence/emo-soc-influ). 
Our findings highlight a critical need for further advancement in the field. As LLM-supported manipulation grows, it is essential to promote public understanding of social influence mechanisms, enabling individuals to critically recognize and interpret the subtle forms of manipulation that shape public opinion.

From Detection to Explanation: Modeling Fine-Grained Emotional Social Influence Techniques with LLMs and Human Preferences

Despite their recent success, the geospatial reasoning capabilities of large language models (LLMs)—which require understanding spatial relationships among real-world geo-entities—remain underexplored.
We propose an automatic method for constructing compositional geographic question answering datasets that jointly consider spatial and entity constraints.
The generated dataset serves as a principled benchmark for evaluating how LLMs coordinate spatial computation with entity-level understanding under diverse compositional settings.
We evaluate two state-of-the-art LLMs, GPT-5.2 and Gemini 3 Flash, on our dataset. Experimental results show that while the models perform relatively well on questions involving rich entity grounding, their accuracy drops substantially on questions requiring precise quantitative spatial reasoning, such as distance estimation and containment judgment.
Our dataset is publicly available for research and reproduction.

Automatic Generation of a Compositional QA Benchmark for Geospatial Reasoning under Spatial and Entity Constraints

Vision Language Action (VLA) models are widely used in Embodied AI, enabling robots to interpret and execute language instructions. However, their robustness to natural language variability in real-world scenarios has not been thoroughly investigated.
In this work, we present a novel systematic study of the robustness of state-of-the-art VLA models under linguistic perturbations. 
Specifically, we evaluate model performance under two types of instruction noise: (1) human-generated paraphrasing and (2) the addition of irrelevant context. We further categorize irrelevant contexts into two groups according to their length and their semantic and lexical proximity to robot commands. In this study, we observe consistent performance degradation as context size expands. We also demonstrate that the model can exhibit relative robustness to random context, with a performance drop within 10\%, while semantically and lexically similar context of the same length can trigger a quality decline of around 50\%. Human paraphrases of instructions lead to a drop of nearly 20\%. Our results highlights a critical gap in the safety and efficiency of modern VLA models for real-world deployment.

Bring the Apple, Not the Sofa: Impact of Irrelevant Context in Embodied AI Commands on VLA Models

Research on bot detection in social media exhibits imbalance in several areas — across platforms, languages, and detection levels. Addressing these gaps, this study focuses on comment-level bot detection within Polish Reddit communities. We describe in detail the construction of a comprehensive dataset (~40,000 comments, 58% bot-comment prevalence), which provides labels for the subsequent model training. Polish Reddit is inherently multilingual, we therefore take advantage of the linguistic signals, treating language composition of a comment as a feature on its own. We develop novel platform-specific, language-specific, and culturally informed features, and train comment-level classifiers from multiple model families on the manually annotated dataset. The resulting models achieve strong performance and temporal generalization to 2025 data. We analyze the importance and direction of these novel features across models and report that our 'cross-level' interaction features, 'Bottiquette' compliance signals, formatting markers, language indicators, repetition and randomness measures — especially the entropy of non-alphabetic characters — rank among the most decisive features. Finally, we complement our quantitative findings with a qualitative characterization of the Polish Reddit bot ecosystem. Overall, this study provides an important baseline for an underexplored setting and contributes to an open discussion on how to approach detection where data is linguistically mixed.

Pioneering Bot Detection on Polish Reddit at the Comment Level

Modern large language model (LLM) systems frequently route inputs to specialized experts to improve accuracy, efficiency, and robustness. Routers determine which expert to activate based on the input, typically represented as a single vector. The construction of this vector limits the distinctions the router can make. Prior work rarely isolates how this vector representation affects routing behavior. We isolate the role of the representation by holding the routing pipeline fixed and vary only how this representation is formed in multilingual settings. We find that representation choice systematically reshapes the available routing partitions. In multilingual routing settings, the routers single-vector input often only encodes shallow features (language/format), resulting in domains that are organized by these features rather than by topic. To mitigate this, we introduce Funnel pooling, a lightweight trainable in-model readout that constructs the routing vector directly from token-level hidden states and does not require a separate embedding encoder. Funnel pooling reduces language and source-dataset driven clustering and results in more topic-aligned domains. Despite this shift, downstream routing performance remains competitive with introducing only a minor inference overhead.

What the Router Sees Matters: Funnel Pooling for Fast, Content Driven Expert Routing

Quantitative text analysis relies on high-quality corpora, but keyword-based collection often retrieves irrelevant material, undermining validity. We show that active learning with a transformer-based classifier can iteratively refine corpora by excluding irrelevant documents, prompting researchers to clarify inclusion criteria and address edge cases. Applied to German newspaper articles on depression and schizophrenia, this approach improves construct validity and reduces labeling effort. The document relevance classifiers reached an F1-score of 0.8 with just 100–150 labeled snippets, with further gains from tuning, outperforming both random sampling and a weakly supervised sampling baseline. Filtering non-medical articles further had little effect on downstream depression stigmatization measures but increased schizophrenia stigmatization. Active learning thus enables efficient corpus validation and clearer concept boundaries with minimal preprocessing. The source code is publicly available at https://github.com/jakobstgl/active-learning-corpus-refinement.

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