China

Extremely low-resource languages, especially those written in rare scripts, remain largely unsupported by large language models (LLMs). This is due in part to compounding factors such as the lack of training data. This paper delivers the first comprehensive analysis of whether LLMs can acquire such languages purely via in-context learning (ICL), with or without auxiliary alignment signals, and how these methods compare to parameter-efficient fine-tuning (PEFT). We systematically evaluate 20 under-represented languages across three state-of-the-art multilingual LLMs. Our findings highlight the limitation of PEFT when both language and its script are extremely under-represented by the LLM. In contrast, zero-shot ICL with language alignment is impressively effective on extremely low-resource languages, while few-shot ICL or PEFT is more beneficial for languages relatively better represented by LLMs. For LLM practitioners working on extremely low-resource languages, we summarise guidelines grounded by our results on adapting LLMs to low-resource languages, e.g., avoiding fine-tuning a multilingual model on languages of unseen scripts.

EMNLP 2025

It&#39;s All About In-Context Learning! Teaching Extremely Low-Resource Languages to LLMs

cross-lingal transfer

multilinguism

less-resourced languages

multilingual evaluation

## 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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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

It's All About In-Context Learning! Teaching Extremely Low-Resource Languages to LLMs

poster

## Welcome!
"I am excited to welcome you to this year’s edition of the Conference on Empirical Methods in Natural Language Processing! Importantly, it marks the 30th edition of EMNLP. With over 8,000 submissions, more than 3,000 accepted papers, and thousands of attendees, we have come a long way from that first
workshop, which had 14 accepted papers. As the field looks ahead, Suzhou is the fitting location for celebrating this milestone: rooted in a long literary tradition, yet modern and forward-looking, and home to a large share of the NLP community." 

*Message from the General Chair, Dirk Hovy*

Celebrate 30 Years of EMNLP! 
EMNLP 2025 will be held in Suzhou, China from November 5th to November 9th, 2025.

As Large Language Models (LLMs) become increasingly prevalent, their generated outputs are proliferating across the web, risking a future where machine-generated content dilutes human-authored text. Since online data is the primary resource for LLM pre-training, subsequent models could be trained on an unknown portion of synthetic samples. This will lead to model collapse, a degenerative process whereby LLMs reinforce their own errors, converge to a low variance output distribution, and ultimately yield a declining performance. In this study, we investigate the impact of decoding strategy on model collapse, analysing the text characteristics at each model generation, the similarity to human references, and the resulting model performance. Using the decoding strategies that lead to the most significant degradation, we evaluate model collapse in more realistic scenarios where the origin of the data (human or synthetic) is unknown. We train a machine-generated text detector and propose an importance sampling approach to alleviate model collapse. Our method is validated on two LLM variants (GPT-2 and SmolLM2), across a range of model sizes (124M to 1.7B), on the open-ended text generation task. We demonstrate that it can not only prevent model collapse but also improve performance when sufficient human-authored samples are present.

Machine-generated text detection prevents language model collapse

We propose Mixture of Languages (MoL), a new strategy to pretrain largely multilingual encoders. Recent work in this field has relied on training transformer encoders on a large amount of multilingual data, with all parameters shared across all languages, without studying how to optimally balance language transfer and interference to achieve better performance. To address this, MoL proposes to group languages based on their similarity, and add parallel, sparsely activated layers that process each group independently. This architecture allows MoL to boost language transfer while minimizing interference, without increasing the active parameter count. We show that MoL largely outperforms a dense counterpart trained with the same configuration, as well as MoE models and public multilingual encoders such as XLM-R or mBERT on downstream tasks.

Mixture of Languages: Improved Multilingual Encoders Through Language Grouping

Arabic dialects form a diverse continuum, yet NLP models often treat them as discrete categories. Recent work addresses this issue by modeling dialectness as a continuous variable, notably through the Arabic Level of Dialectness (ALDi). However, ALDi reduces complex variation to a single dimension. We propose a complementary measure: the Arabic Generality Score (AGS), which quantifies how widely a word is used across dialects. We introduce a pipeline that combines word alignment, etymology-aware edit distance, and smoothing to annotate a parallel corpus with word-level AGS. A regression model is then trained to predict AGS in context. Our approach outperforms strong baselines, including state-of-the-art dialect ID systems, on a multi-dialect benchmark. AGS offers a scalable, linguistically grounded way to model lexical generality, enriching representations of Arabic dialectness.

The Arabic Generality Score: Another Dimension of Modeling Arabic Dialectness

Despite Greece’s pivotal role in the global economy, large language models (LLMs) remain underexplored for Greek financial context due to the linguistic complexity of Greek and the scarcity of domain-specific datasets. While multilingual financial NLP has revealed large performance gaps across languages, no benchmarks or LLMs have been tailored for Greek financial tasks until now. To bridge this gap, we introduce Plutus-ben, the first Greek Financial Evaluation Benchmark, and Plutus-8B, the first financial LLM fine-tuned on Greek-specific financial data. Plutus-ben addresses five core tasks: numeric/textual named entity recognition, question answering, abstractive summarization, and topic classification. To support these tasks, we release three new expert-annotated Greek financial datasets and incorporate two existing resources. Our comprehensive evaluation of 22 LLMs reveals persistent challenges in Greek financial NLP, driven by linguistic complexity, domain terminology, and financial reasoning gaps. Experiment results underscore the limitations of cross-lingual transfer and the need for Greek-specific financial modeling. We publicly release Plutus-ben, Plutus-8B, and all associated datasets to promote reproducible research and advance multilingual financial NLP.

Plutus: Benchmarking Large Language Models in Low-Resource Greek Finance

Reinforcement learning with verifiable rewards (RLVR) has become a key technique for enhancing large language models (LLMs), with verification engineering playing a central role. However, best practices for RL in instruction following remain underexplored. In this work, we explore the verification challenge in RL for instruction following and propose VerIF, a verification method that combines rule-based code verification with LLM-based verification from a large reasoning model (e.g., QwQ-32B). To support this approach, we construct a high-quality instruction-following dataset, VerInstruct, containing approximately 22,000 instances with associated verification signals. We apply RL training with VerIF to two models, achieving significant improvements across several representative instruction-following benchmarks. The trained models reach state-of-the-art performance among models of comparable size and generalize well to unseen constraints. We further observe that their general capabilities remain unaffected, suggesting that RL with VerIF can be integrated into existing RL recipes to enhance overall model performance. We will release our datasets, codes, and models to facilitate future research.

VerIF: Verification Engineering for Reinforcement Learning in Instruction Following

Rapid deployment of vision-language models (VLMs) magnifies safety risks, yet most evaluations rely on artificial images. This study asks: How safe are current VLMs when confronted with meme images that ordinary users share? To investigate this question, we introduce MemeSafetyBench, a 50,430-instance benchmark pairing real meme images with both harmful and benign instructions. Using a comprehensive safety taxonomy and LLM-based instruction generation, we assess multiple VLMs across single and multi-turn interactions. We investigate how real-world memes influence harmful outputs, the mitigating effects of conversational context, and the relationship between model scale and safety metrics. Our findings demonstrate that VLMs show greater vulnerability to meme-based harmful prompts than to synthetic or typographic images. Memes significantly increase harmful responses and decrease refusals compared to text-only inputs. Though multi-turn interactions provide partial mitigation, elevated vulnerability persists. These results highlight the need for ecologically valid evaluations and stronger safety mechanisms.

Are Vision-Language Models Safe in the Wild? A Meme-Based Benchmark Study

We explore the possibility of semantic networks as a diagnostic tool for cognitive decline by using Dutch verbal fluency data to investigate the relationship between semantic networks and cognitive health. We explore construction of these networks using the contextual word embedding models BERTje (Dutch) and XLMRoBERTa (multilingual) and static embedding model FastText. In psychology, semantic networks serve as abstract representations of the semantic memory system. Semantic verbal fluency data can be used to estimate said networks. Traditionally, this is done by counting the number of raw items produced by participants in a verbal fluency task. We used the models to connect the elicited words through semantic similarity scores, and extracted three network distance metrics in addition to the traditional approach of counting. We then tested how well these metrics predict participants' cognitive health scores on the Mini-Mental State Examination (MMSE). We find that the centroid-diameter and average distance metrics are significant predictors of cognitive health scores in our data, with the traditional scoring method not reaching significance.

Mapping semantic networks to Dutch word embeddings as a diagnostic tool for cognitive decline

As AI systems increasingly navigate applications in healthcare, law, and governance, understanding how they handle ethically complex scenarios becomes critical. Previous work has mainly examined the moral judgments in large language models (LLMs), rather than their underlying moral reasoning process. In contrast, we focus on a large-scale analysis of the moral reasoning traces provided by LLMs. Furthermore, unlike prior work that attempted to draw inferences from only a handful of moral dilemmas, our study leverages over 600 distinct trolley problems as probes for revealing the reasoning patterns that emerge within different LLMs. We introduce and test a taxonomy of moral rationales to systematically classify reasoning traces according to two main normative ethical theories: consequentialism and deontology. Our analysis reveals that LLM chains-of-thought favor deontological principles based on moral obligations, while post-hoc explanations shift notably toward consequentialist rationales that emphasize utility. Our framework provides a foundation for understanding how LLMs process and articulate ethical considerations, an important step toward safe and interpretable deployment of LLMs in high-stakes decision-making environments.

Are Language Models Consequentialist or Deontological Moral Reasoners?

Few-shot knowledge graph (KG) relational learning aims to perform reasoning over relations with only a handful of training examples. While most existing methods for few-shot relational learning have primarily focused on leveraging relational information, rich semantics inherent in KGs have been largely overlooked. To address this gap, we propose a novel prompted meta-learning (PromptMeta) framework that seamlessly integrates meta-semantics with relational information for few-shot relational learning. PrompMeta has two key innovations: (1) a Meta-Semantic Prompt (MSP) pool that consolidates high-level meta-semantics, enabling effective knowledge transfer and adaptation to rare and newly emerging relations. (2) a learnable fusion token that dynamically combines meta-semantics with task-specific relational information tailored to different few-shot tasks. Both components are optimized jointly with model parameters within a meta-learning framework. Extensive experiments and analyses on two real-world KG datasets demonstrate the effectiveness of our approach. The code and datasets are available at: https://github.com/A20250216/ARR02.

Meta-Semantics Augmented Few-Shot Relational Learning

This paper investigates prior prompt engineering (pPE) in the context of reinforcement fine-tuning (RFT), where language models (LMs) are incentivized to exhibit behaviors that maximize performance through reward signals. While existing RFT research has primarily focused on algorithms, reward shaping, and data curation, the design of the prior prompt--the instructions prepended to queries during training to elicit behaviors such as step-by-step reasoning--remains underexplored. We investigate whether different pPE approaches can guide LMs to internalize distinct behaviors after RFT. Inspired by inference-time prompt engineering (iPE), we translate five representative iPE strategies--reasoning, planning, code-based reasoning, knowledge recall, and null-example utilization--into corresponding pPE approaches. We experiment with Qwen2.5-7B using each of the pPE approaches, then evaluate performance on in-domain and out-of-domain benchmarks (e.g., AIME2024, HumanEval+, and GPQA-Diamond). Our results show that all pPE-trained models surpass their iPE-prompted counterparts, with the null-example pPE approach achieving the largest average performance gain and the highest improvement on AIME2024 and GPQA-Diamond, surpassing the commonly used reasoning approach. Furthermore, by adapting a behavior-classification framework, we demonstrate that different pPE strategies instill distinct behavioral styles in the resulting models. These findings position pPE as a powerful yet understudied axis for RFT.

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