Morocco

Text Generation has achieved remarkable performance using large language models. It has also been recently well-studied that these large language models are capable of creative generation tasks but prominently for high-resource languages. This prompts a fundamental question: Is there a way to utilize these (large) language models for structured poetry generation in a low-resource language, such as Sanskrit? We present Chandomitra, an English input to structured Sanskrit Poetry translation dataset, specifically adhering to the Anushtubh meter. We benchmark various open and closed models, and scrutinize specialized techniques such as constrained decoding and instruction fine-tuning, for the proposed task. Our constrained decoding methodology achieves 99.86% syntactic accuracy in generating metrically valid Sanskrit poetry, outperforming GPT-4o (1-shot: 31.24%). Our best-performing instruction-tuned model, on the other hand, performs better in semantic coherence with the English input, at the expense of slightly lower syntactic accuracy. Human evaluation further reveals that instruction fine-tuned model is better able to capture the poetic aspects.

EACL 2026

Chandomitra: Towards Generating Structured Sanskrit Poetry from Natural Language Inputs

constrained-decoding

poetry-generation

instruction-tuning

sanskrit

### Welcome to IJCNLP-AACL 2025! 
 It is a great honor to host this joint conference in Mumbai, India, from December 20 to 24, 2025. The joint conferences of IJCNLP and AACL are organized with alternating leadership in the Asia-Pacific region. The event is run by the Asian Federation of Natural Language Processing (AFNLP) in odd years, and by AACL in even years, while it is organized solely by ACL when the annual ACL meeting is held in the region. This year, the conference is primarily organized by AFNLP. 
*Kentaro Inui
MBZUAI, UAE
General Chair, IJCNLP-AACL 2025* 
Read full message and download the Conference Handbook [**here**](https://drive.google.com/file/d/1UTwxkAqSqI-GAoJC3wE1zZt5VP1Y8GX0/view?usp=sharing).

IJCNLP-AACL 2025

The 14th IJCNLP & 4th AACL will be held in Mumbai, India from December 20th to December 24th, 2025.

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)* 

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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. 

Despite the rapid advancements of Large Language Models (LLMs), safety risks remain a critical challenge for low-resource languages. Existing safety datasets are predominantly English-centric, limiting progress in multilingual safety alignment. As a result, low-resource expert modelsвЂ”fine-tuned on their respective instruction datasetsвЂ”tend to exhibit higher unsafety rates compared to their high-resource counterparts. In this work, we propose a safety aware layer swapping method that transfers safety alignment from an English safety expert to low-resource language experts without additional training. To further enhance transfer ability, our method adaptively selects or blends modules based on their degree of specialization. Our approach preserves performance on general language understanding tasks while enhancing safety in the target languages. Experimental results show that the proposed method achieves comparable performance to the language expert on general benchmarks such as MMMLU, BELEBELE, and MGSM, while producing more aligned and less harmful responses on the MultiJail safety benchmark

Layer-wise Swapping for Generalizable Multilingual Safety

Large language models (LLMs) demonstrate superior reasoning capabilities compared to small language models (SLMs), but incur substantially higher costs. We propose COllaborative REAsoner (COREA), a system that cascades an SLM with an LLM to achieve a balance between accuracy and cost in complex reasoning tasks. COREA first attempts to answer questions using the SLM, which outputs both an answer and a verbalized confidence score. Questions with confidence below a predefined threshold are deferred to the LLM for more accurate resolution. We introduce a reinforcement learning-based training algorithm that aligns the SLM's confidence through an additional confidence calibration reward. Extensive experiments demonstrate that our method jointly improves the SLM's reasoning ability and confidence calibration across diverse datasets and model backbones. Compared to using the LLM alone, COREA reduces cost by 21.5% and 16.8% on out-of-domain math and non-math datasets, respectively, with only an absolute pass@1 drop within 2%.

Confidence-Calibrated Small-Large Language Model Collaboration for Cost-Efficient Reasoning

Large language models (LLMs) are increasingly used to access legal information. Yet, their deployment in multilingual legal settings is constrained by unreliable retrieval and the lack of domain-adapted, open-embedding models. In particular, existing multilingual legal corpora are not designed for semantic retrieval, and PDF-based legislative sources introduce substantial noise due to imperfect text extraction. To address these challenges, we introduce LEMUR, a large-scale multilingual corpus of EU environmental legislation constructed from 24,953 official EUR-Lex PDF documents covering 25 languages. We further propose the Lexical Content Score (LCS), a language-agnostic metric that quantifies the fidelity of PDF-to-text conversion by measuring lexical consistency against authoritative HTML versions. Building on LEMUR, we fine-tune three state-of-the-art multilingual embedding models using contrastive objectives in both monolingual and bilingual settings, reflecting realistic legal-retrieval scenarios. Experiments across low- and high-resource languages demonstrate that legal-domain fine-tuning consistently improves Top-k retrieval accuracy relative to strong baselines, with particularly pronounced gains for low-resource languages. Cross-lingual evaluations show that these improvements transfer to unseen languages, indicating that fine-tuning primarily enhances language-independent, content-level legal representations rather than language-specific cues. 
We publish code\footnote{\href{https://github.com/nargesbh/eur_lex}{GitHub Repository}} and data\footnote{\href{https://huggingface.co/datasets/G4KMU/LEMUR}{Hugging Face Dataset}}.

LEMUR: Robust Fine-Tuning for Multilingual Embedding Models for Retrieval

Large Language Models (LLMs) excel across diverse NLP tasks but remain too large for efficient on-device deployment. Although knowledge distillation offers a promising compression strategy, direct one-step distillation from a large teacher to a small student often leads to substantial performance loss due to the capacity gap. In this work, we revisit multi-step knowledge distillation (MSKD) as an effective remedy, exploring how staged, size-aware transfer paths can better preserve teacher knowledge across students of varying scales. Through extensive experiments with GPT-2 and OPT, we demonstrate that MSKD consistently improves ROUGE-L and perplexity over single-step approaches without requiring specialized fine-tuning. Our results establish multi-step transfer as a simple yet powerful framework for progressively compressing LLMs into efficient, high-performing Small Language Models (SLMs).

Beyond One-Step Distillation: Bridging the Capacity Gap in Small Language Models via Multi-Step Knowledge Transfer

Large language models (LLMs) can generate fluent text, but the quality of generated content crucially depends on its consistency with the given input.
This aspect is commonly referred to as faithfulness, which concerns whether the output is properly grounded in the input context.
A major challenge related to faithfulness is that generated content may include information not supported by the input or may contradict it.
This phenomenon is often referred to as hallucination, and increasing attention has been paid to automatic hallucination detection, which determines whether an LLM's output is hallucinated.
To evaluate the performance of hallucination detection systems, researchers use evaluation datasets with labels indicating the presence or absence of hallucinations.
While such datasets have been developed for English and Chinese, Japanese evaluation resources for hallucination detection remain limited.

Therefore, we constructed a Japanese evaluation dataset for hallucination detection in summarization by manually annotating sentence-level faithfulness labels in LLM-generated summaries of Japanese documents.
We annotate 390 summaries (1,938 sentences) generated by three LLMs with sentence-level multi-label annotations for faithfulness with respect to the input document.

The taxonomy extends a prior classification scheme and captures distinct patterns of model errors, enabling both binary hallucination detection and fine-grained error-type analysis of Japanese LLM summarization.

Constructing a Dataset for Hallucination Detection in Japanese Summarization with Fine-grained Faithfulness Labels

Conversational question answering increasingly relies on retrieval-augmented generation (RAG) to ground large language models (LLMs) in external knowledge. Yet, most existing studies evaluate RAG methods in isolation and primarily focus on single-turn settings. This paper addresses the lack of a systematic comparison of RAG methods for multi-turn conversational QA, where dialogue history, coreference, and shifting user intent substantially complicate retrieval. We present a comprehensive empirical study of vanilla and advanced RAG methods across eight diverse conversational QA datasets spanning multiple domains. Using a unified experimental setup, we evaluate retrieval quality and answer generation using generator and retrieval metrics, and analyze how performance evolves across conversation turns. Our results show that robust yet straightforward methods, such as reranking, hybrid BM25, and HyDE, consistently outperform vanilla RAG. In contrast, several advanced techniques fail to yield gains and can even degrade performance below the No-RAG baseline. We further demonstrate that dataset characteristics and dialogue length strongly influence retrieval effectiveness, explaining why no single RAG strategy dominates across settings. Overall, our findings indicate that effective conversational RAG depends less on method complexity than on alignment between the retrieval strategy and the dataset structure. We publish the code used.\footnote{\href{https://github.com/Klejda-A/exp-rag.git/}{GitHub Repository}}

Comprehensive Comparison of RAG Methods Across Multi-Domain Conversational QA

LLM-based assistants have been widely popularised after the release of ChatGPT. Concerns have been raised about their misuse in academia, given the difficulty of distinguishing between human-written and generated text. To combat this, automated techniques have been developed and shown to be effective, to some extent. However, prior work suggests that these methods often falsely flag essays from non-native speakers as generated, due to their low perplexity extracted from an LLM, which is supposedly a key feature of the detectors. We revisit these statements two years later, specifically in the Czech language setting. We show that the perplexity of texts from non-native speakers of Czech is not lower than that of native speakers. We further examine detectors from three separate families and find no systematic bias against non-native speakers. Finally, we demonstrate that contemporary detectors operate effectively without relying on perplexity.

Different Time, Different Language: Revisiting the Bias Against Non-Native Speakers in GPT Detectors

We study model routing for Large Language Model (LLM)-based systems. A model, called the router, dynamically chooses which LLM should handle a given input/query. We challenge the assumption that complex routers are necessary for generalising to new candidate LLMs. We introduce ContextualRouter, a simple meta-evaluation framework that predicts per-model performance for new queries by retrieving similar past queries and reweighting model scores with lightweight attention. During inference, the router balances estimated performance and cost by adjusting a tunable cost penalty parameter. This allows the router to adapt dynamically to the addition or removal of LLMs without the need for retraining. \n\nAcross five routing benchmarks (SPROUT, RouterBench, LiveBench, BigGenBench, and EmbedLLM), ContextualRouter matches the quality–cost trade-offs of other generalisable routers. Surprisingly, a simpler non-parametric baseline, k-nearest-neighbour averaging, performs comparably or better, achieving strong performance estimation, high NDCG, and substantial cost savings. Retrieval-based routers remain robust to k, embedding size, data sparsity, retrieval degradation, and generalise to unseen queries and models with as little as 1% historical data. These results suggest that effective retrieval alone enables generalisable LLM routing.

Generalising LLM Routing using Past Performance Retrieval: A Few-Shot Router is Sufficient

While the semantic space has been examined as a way to computationally represent language meaning-grammar interface, minimal research has been done comparing the semantic spaces of first and second language learners. We investigated the semantic space of university-level students learning French by extracting semantic features from narrative text over various time points from a 21-month period. After using machine learning models to classify native speakers' semantic features from second language learners', we used interpretability techniques to identify the most informative features per model. Through this, we discovered a variety of embedding similarity features to be decisive in language learning. We compared both groups to determine how the features differed per group and if there was any change over time. The findings demonstrated that the second language learners on average had higher semantic similarity scores than the native speakers at the token level. The similarity decreased over time but did not reach native-level values. Similarly, average surprisal was higher in the second language learner group, which steadily decreased over the course of the data collection period. These results provide insight into personalized education with more precise and effective computational indices tracking learners' progress.

Exploring the Semantic Space of Second Language Learners

While Large Language Models demonstrate expert proficiency on medical benchmarks, the clinical encounter requires more than factual retrieval. It demands a sophisticated rhetorical performance of care that balances authority with epistemic humility. This paper investigates the Clinical Fingerprint by comparing the structural and ethical integrity of advice generated by human physicians and various language models.


Our findings reveal a fundamental divergence in how clinical information is prioritized and delivered. We show that whereas physicians utilize efficient, action-oriented structures to provide clear guidance, generic models often bury critical advice under layers of complex linguistic recursion. This creates a significant cognitive load for patients and risks a dangerous safety cliff where models adopt an unearned authoritative tone. Such models frequently mimic the confidence of a doctor while providing contradictory advice, particularly in complex cases involving multiple symptoms. By identifying these rhetorical gaps, our work emphasizes that domain-specific fine-tuning is an ethical necessity to ensure that AI assistants maintain the necessary humility and logical cohesion required for safe medical practice.

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Layer-wise Swapping for Generalizable Multilingual Safety

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