Morocco

Large language models (LLMs) encode rich cultural knowledge learned from diverse web-scale data, offering an unprecedented opportunity to model cultural commonsense at scale. Yet this knowledge remains mostly implicit and unstructured, limiting its interpretability and use. We present an iterative, prompt-based framework for constructing a Cultural Commonsense Knowledge Graph (CCKG) that treats LLMs as cultural archives, systematically eliciting culture-specific entities, relations, and practices and composing them into multi-step inferential chains across languages. We evaluate CCKG on five countries with human judgments of cultural relevance, correctness, and path coherence. We find that the cultural knowledge graphs are better realized in English, even when the target culture is non-English (e.g., Chinese, Indonesian, Arabic), indicating uneven cultural encoding in current LLMs. Augmenting smaller LLMs with CCKG improves performance on cultural reasoning and story generation, with the largest gains from English chains. Our results show both the promise and limits of LLMs as cultural technologies and that chain-structured cultural knowledge is a practical substrate for culturally grounded NLP.

EACL 2026

LLMs as Cultural Archives: Cultural Commonsense Knowledge Graph Extraction

structures cultural knowledge

cultural commonsense knowledge graph extraction

cultural reasoning

llms

multilingualism

culture

commonsense reasoning

## Welcome to 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics!
This year, the conference is in Mexico City. NAACL was actually already planned for Mexico City in 2021, but due to the pandemic the entire conference was moved online. This year, finally, we get to go! So it is my sincere pleasure to welcome you to Mexico City, whether in person or virtually. Having the conference in Mexico City is a good opportunity to emphasize that NAACL is our flagship conference for ACL members not only in North America but also in Central and South America, even though NAACL has been bearing “North” in its name. At this year’s conference, we have a theme to match, with a theme track on the Languages of Latin America to showcase the linguistic diversity of the region.
 
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Welcome and hope you all enjoy the conference! 
Katrin Erk 
The University of Texas at Austin 
NAACL-2024 General Chair 
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NAACL 2024

2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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#### *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. 

Modern conversational AI systems require sophisticated Named Entity Recognition (NER) capabilities that can handle complex, contextual dialogue patterns. While Large Language Models (LLMs) excel at understanding conversational semantics, their inference latency and inability to efficiently incorporate emerging entities make them impractical for production deployment. Moreover, the scarcity of conversational NER data creates a critical bottleneck for developing effective models. We address these challenges through two main contributions. First, we introduce an automated pipeline for generating multilingual conversational NER datasets with minimal human validation, producing 4,082 English and 3,925 Spanish utterances. Second, we present a scalable framework that leverages LLMs as semantic filters combined with catalog-based entity grounding to label live traffic data, enabling knowledge distillation into faster, production-ready models. On internal conversational datasets, our teacher model demonstrates 39.55% relative F1-score improvement in English and 44.93% in Spanish compared to production systems. On public benchmarks, we achieve 97.12% F1-score on CoNLL-2003 and 83.09% on OntoNotes 5.0, outperforming prior state-of-the-art by 24.82 and 8.19 percentage points, respectively. Finally, student models distilled from our teacher approach achieve 13.84% relative improvement on English conversational data, bridging the gap between LLM capabilities and real-world deployment constraints.

When Speed Meets Intelligence: Scalable Conversational NER in an Ever-evolving World

Workflow automation is critical for reducing manual efforts in industries, yet existing pipelines fail to handle generative tasks like summarization and extraction without pre-built tools, forcing human intervention. While LLM-based agents offer solutions, their creation depends heavily on prompt engineering—a resource-intensive process often yielding suboptimal results. Current automated approaches face a fundamental trade-off: discrete optimization produces overfitted prompts without convergence guarantees due to non-convex landscapes, while continuous gradient-based methods generate semantically incoherent prompts through embedding optimization. We propose CASPER, a framework bridging discrete and continuous prompt optimization through feedback-guided gradient descent in embedding space. CASPER employs a feedback module producing detailed error analyses that capture failure modes as optimization signals. These insights are projected with prompt tokens into embedding space to steer gradient descent. To preserve interpretability, we incorporate fluency regularization that penalizes incomprehensible tokens. We further accelerate convergence through synthetic data generation that oversamples failure cases, while also addressing data scarcity in industrial settings. We evaluate CASPER on WDC, DROP, GSM8K with F1 improvements of 2.3\%, 1.6\%, 2.3\% and VQA, internal benchmarks showing accuracy improvements of 1.1\%, 3\%, demonstrating cross-domain generalizability.

CASPER: Bridging Discrete and Continuous Prompt Optimization through Feedback-Guided Gradient Descent

Variation in human annotation (i.e., disagreements) is common in NLP, often reflecting important information like task subjectivity and sample ambiguity. Modeling this variation is important for applications that are sensitive to such information. Although RLVR-style reasoning (Reinforcement Learning with Verifiable Rewards) has improved Large Language Model (LLM) performance on many tasks, it remains unclear whether such reasoning enables LLMs to capture informative variation in human annotation. In this work, we evaluate the influence of different reasoning settings on LLM disagreement modeling. We systematically evaluate each reasoning setting across model sizes, distribution expression methods, and steering methods, resulting in 60 experimental setups across 3 tasks. Surprisingly, our results show that RLVR-style reasoning degrades performance in disagreement modeling, while naive Chain-of-Thought (CoT) reasoning improves the performance of RLHF LLMs (RL from human feedback). These findings underscore the potential risk of replacing human annotators with reasoning LLMs, especially when disagreements are important.

Can Reasoning Help Large Language Models Capture Human Annotator Disagreement?

End-to-end form filling refers to automatically populating fields in a document-style form with the appropriate information derived from external data. Although prevalent and useful, no formal benchmark exists for evaluating systems' form completion accuracy. Existing datasets focus on parsing, extraction and web form interaction, rather than end-to-end completion of document-style forms. We propose FormGym, a benchmark formulation of the end-to-end form filling task that evaluates form completion and accuracy. We construct FormGym by repurposing three existing datasets and add one new dataset to achieve more challenging, diverse, and realistic test cases. Our studies show baseline vision language agents (VLAs) perform poorly on FormGym in every scenario, primarily due to poor field localization. GUI agents perform better but suffer from high latency and costs. Therefore we also introduce FieldFinder, a field localization tool that enables zero-shot VLAs to find and accurately place text in input fields. We find that VLAs augmented with FieldFinder achieve better performance compared to baselines in all models.

FormGym: Doing Paperwork with Agents

Quantization is an effective technique for reducing the storage footprint and computational costs of Large Language Models (LLMs), but it often results in performance degradation. Existing post-training quantization methods typically use small, English-only calibration sets; however, their impact on multilingual models remains underexplored. We systematically evaluate eight calibration settings (five single-language and three multilingual mixes) across two quantizers (GPTQ, AWQ) on data from 10 different languages. Our findings reveal a consistent trend: non-English and multilingual calibration sets significantly improve perplexity compared to English-only baselines. Specifically, we observe notable average perplexity gains across both quantizers on Llama3.1 8B and Qwen2.5 7B, with multilingual mixes achieving the largest overall reductions of up to 3.52 perplexity gain. Furthermore, our analysis indicates that tailoring calibration sets to the evaluation language yields the largest improvements for individual languages, underscoring the importance of linguistic alignment. We also identify specific failure cases where certain language-quantizer combinations degrade performance, which we trace to differences in activation range distributions across languages. These results highlight that static, one-size-fits-all calibration is suboptimal, and that tailoring calibration data, both in language and diversity, plays a crucial role in robustly quantizing multilingual LLMs.

Calibrating Beyond English: Language Diversity for Better Quantized Multilingual LLMs

Retrieval Augmented Generation (RAG) is a popular method for injecting up-to-date information into Large Language Model (LLM)-based Natural Language Generation (NLG) systems. While RAG can enhance factual accuracy, its effect on the social biases inherent in LLMs is not well understood. This paper systematically investigates how RAG modulates social biases across three languages (English, Japanese, and Chinese) and four categories (gender, race, age, and religion). By evaluating various generator LLMs on the BBQ benchmark, we analyse how document collections with controlled stereotypical content affect RAG outputs. We find that biases present in the retrieved documents are often significantly amplified in the generated texts, even when the base LLM itself has a low-level of intrinsic bias. These findings raise concerns about the social fairness of RAG systems, underscoring the urgent need for careful bias evaluation before real-world deployment.

Evaluating the Effect of Retrieval Augmentation on Social Biases

The promise of LLM-based user simulators to improve conversational AI is hindered by a critical "realism gap," leading to systems that are optimized for simulated interactions, but may fail in the real world. We introduce ConvApparel, a new dataset of human-AI conversations designed to better understand this gap. Its unique dual-agent data collection protocol, using both "good" and "bad" recommenders, enables counterfactual validation by capturing a wide spectrum of user experiences, enriched with first-person annotations of user satisfaction. We propose a comprehensive validation framework that combines statistical alignment, a human-likeness score, and counterfactual validation to test for generalization. Our experiments reveal a significant realism gap across all simulators. However, the framework also shows that data-driven methods consistently outperform a prompted baseline, particularly in counterfactual validation where they adapt more realistically to unseen behaviors, indicating a more robust, if imperfect, user model.

ConvApparel: A Benchmark Dataset and Validation Framework for User Simulators in Conversational Recommenders

In this paper, we study the expansion of pluralia tantum, i.e., defective nouns which lack a singular form, like scissors. We base our work on an annotation framework specifically developed for the study of lexicalization of pluralia tantum, namely Lexicalization profiles. On a corresponding hand-annotated testset, we show that the OpenAI and DeepSeek models provide useful annotators for semantic, syntactic and sense categories, with accuracy ranging from 51% to 89%, averaged across all feature groups and languages. Next, we turn to a large-scale investigation of pluralia tantum. Using dictionaries, we extract candidate words for Italian, Russian and English and keep those for which the changing ratio of singular and plural form is evident in a corresponding reference corpus. We use an LLM to annotate each instance from the reference corpora according to the annotation framework. We show that the large amount of automatically annotated sentences for each feature can be used to perform in-depth linguistic analysis. Focusing on the correlation between an annotated feature and the grammatical form (singular vs. plural), patterns of morpho-semantic change are noted.

Elections go bananas: a first large-scale multilingual study of pluralia tantum using LLMs

In continual learning, our aim is to learn a new task without forgetting what was learned previously. In topic models, this translates to learning new topic models without forgetting previously learned topics. Previous work either considered Dynamic Topic Models (DTMs), which learn the evolution of topics based on the entire training corpus at once, or Online Topic Models, which are updated continuously based on new data but do not have long-term memory. To fill this gap, we propose the Continual Neural Topic Model (CoNTM), which continuously learns topic models at subsequent time steps without forgetting what was previously learned. This is achieved using a global prior distribution that is continuously updated. In our experiments, CoNTM consistently outperformed the dynamic topic model in terms of topic quality and predictive perplexity while being able to capture topic changes online. The analysis reveals that CoNTM can learn more diverse topics and better capture temporal changes than existing methods.

Continual Neural Topic Model

The medical adoption of NLP tools requires interpretability by end users, yet traditional XAI methods are misaligned with clinical reasoning and lack clinician input. We introduce CHiRPE (Clinical High-Risk Prediction with Explainability), an NLP pipeline that takes transcribed semi-structured clinical interviews to (i) predict psychosis risk and (ii) generate novel SHAP explanation formats co-developed with clinicians. Trained on 944 semi-structured interview transcripts across 24 international clinics of AMP-SCZ study, the CHiRPE pipeline integrates symptom-domain mapping, LLM summarisation and BERT classification, achieving above 90% accuracies across all three BERT variants, outperforming baseline models. Evaluation of explanation formats by twenty-eight clinical experts demonstrated strong preference for our novel concept-guided explanations, especially hybrid graph and text summary formats, over standard SHAP word-level bar plots and heat map visualisations. CHiRPE demonstrates that clinically-guided model development produces both accurate and interpretable results. Our next step is focused on real-world testing across our 24 international sites.

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When Speed Meets Intelligence: Scalable Conversational NER in an Ever-evolving World

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Understanding the Capabilities and Limitations of Large Language Models for Cultural Commonsense

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