Morocco

We evaluate the effectiveness of synthetic data fine-tuning for Semantic Search in a real-world Enterprise Team Formation problem scenario. In this problem, we aim to retrieve the best employee for a given task, given their information regarding abilities, experiences, and other aspects. We evaluate two synthetic data generation strategies: (1) augmenting real-world data with synthetic labels and (2) generating synthetic profiles for employees tailored to specific tasks. To measure the impact of these strategies, we fine-tune a pretrained text embedding model using LoRA and Rank Aggregation techniques. We evaluate the model performance against current SOTA algorithms on a human-curated dataset. Our experiments indicate that training a model that uses a combination of both Synthetic data generation strategies outperforms already established pre-trained models on the Team Formation task, improving the ranking metrics by an average of 30\% in comparison to the best-performing pre-trained model.

EACL 2026

Synthetic Data Fine-Tuning for Effective Team Formation in Enterprises

nlp in resource-constrained settings

financial/business nlp

dense retrieval

**Welcome to EMNLP 2023!**

On behalf of the EMNLP 2023 Organizing Committee, I extend a warm and heartfelt welcome to all of you to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP). It is with immense pleasure and excitement that we gather here in Singapore, a vibrant hub of innovation and technological advancement.

The conference program is packed with insightful presentations, thought-provoking workshops, and engaging networking opportunities. In addition to the technical sessions, we have also planned several social events that will provide you with the opportunity to connect with your colleagues.

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Yuji Matsumoto (RIKEN AIP) 
EMNLP 2023 General Chair

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EMNLP 2023

EMNLP 2023 took place in Singapore from Dec 6th to Dec 10th, 2023.

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. 

Legal documents have complex document layouts involving multiple nested sections, lengthy footnotes and further use specialized linguistic devices like intricate syntax and domain-specific vocabulary to ensure precision and authority. These inherent characteristics of legal documents make question answering challenging, and particularly so when the answer to the question spans several pages (i.e. requires long-context) and is required to be comprehensive (i.e. a long-form answer). In this paper, we address the challenges of long-context question answering in context of long-form answers given the idiosyncrasies of legal documents. We propose a question answering system that can (a) deconstruct domain-specific vocabulary for better retrieval from source documents, (b) parse complex document layouts while isolating sections and footnotes and linking them appropriately, (c) generate comprehensive answers using precise domain-specific vocabulary. We also introduce a coverage metric that classifies the performance into recall-based coverage categories allowing human users to evaluate the recall with ease. By leveraging the expertise of professionals from fields such as law and corporate tax, we curate a QA dataset. Through comprehensive experiments and ablation studies, we demonstrate the usability and merit of the proposed system.

Long-Context Long-Form Question Answering for Legal Domain

We propose an efficient layer-specific optimization (ELO) method designed to enhance continual pretraining (CP) for specific languages in multilingual large language models (MLLMs). This approach addresses the common challenges of high computational cost and degradation of source language performance associated with traditional CP. The ELO method consists of two main stages: (1) ELO Pretraining, where a small subset of specific layers, identified in our experiments as the critically important first and last layers, are detached from the original MLLM and trained with the target language. This significantly reduces not only the number of trainable parameters but also the total parameters computed during the forward pass, minimizing GPU memory consumption and accelerating the training process. (2) Layer Alignment, where the newly trained layers are reintegrated into the original model, followed by a brief full fine-tuning step on a small dataset to align the parameters. Experimental results demonstrate that the ELO method achieves a training speedup of up to 6.46 times compared to existing methods, while improving target language performance by up to 6.2\% on qualitative benchmarks and effectively preserving source language (English) capabilities.

ELO: Efficient Layer-Specific Optimization for Continual Pretraining of Multilingual LLMs

Despite the state-of-the-art performance of Large Language Models (LLMs) achieved on many tasks, their massive scale often leads to high computational and environmental costs, limiting their accessibility. Parameter-efficient fine-tuning (PEFT) methods address this challenge by reducing the number of trainable parameters while maintaining strong downstream performance. Despite the increased development in PEFT methods, current evaluations remain limited (in terms of evaluated models and datasets) and difficult to reproduce. To bridge this gap, we introduce PEFT-Bench, a unified end-to-end benchmark for evaluating diverse PEFT methods on autoregressive LLMs. We demonstrate its usage across 27 NLP datasets and 6 PEFT methods. To account for different PEFT training and inference factors, we also introduce the PEFT Soft Score Penalties (PSCP) metric, which takes trainable parameters, inference speed, and training memory usage into account.

PEFT-Bench: A Parameter-Efficient Fine-Tuning Methods Benchmark

Since many real-world documents combine textual and tabular data, robust Retrieval Augmented Generation (RAG) systems are essential for effectively accessing and analyzing such content to support complex reasoning tasks. Therefore, this paper introduces T2-RAGBench, a benchmark comprising 23,088 question-context-answer triples, designed to evaluate RAG methods on real-world text-and-table data. Unlike typical QA datasets that operate under Oracle-Context settings, T2-RAGBench challenges models to first retrieve the correct context before conducting numerical reasoning. Existing QA datasets containing text-and-table data typically contain context-
dependent questions, which may yield multiple correct answers depending on the provided context. To address this, we transform SOTA
datasets into a context-independent format, validated by experts as 91.3% context-independent questions, enabling reliable RAG evaluation.
Our comprehensive evaluation identifies Hybrid BM25 , a technique that combines dense and sparse vectors, as the most effective approach for
text-and-table data. However, results demonstrate that T2-RAGBench remains challenging even for SOTA LLMs and RAG methods. Further ablation studies examine the impact of embedding models and corpus size on retrieval performance. T2-RAGBench provides a realistic and rigorous benchmark for existing RAG methods on text-and-table data. Code and dataset are available online: https://anonymous.4open.science/r/g4kmu-paper-D5F8/README.md.

T2-RAGBench: Text-and-Table Aware Retrieval-Augmented Generation

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.

Chandomitra: Towards Generating Structured Sanskrit Poetry from Natural Language Inputs

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.

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