United States

LLMs are increasingly being deployed for multilingual applications and have demonstrated impressive translation capabilities between several low and high-resource languages. An aspect of translation that often gets overlooked is that of cultural adaptation, or modifying source culture references to suit the target culture. While specialized translation models still outperform LLMs on the machine translation task when viewed from the lens of correctness, they are not sensitive to cultural differences often requiring manual correction. LLMs on the other hand have a rich reservoir of cultural knowledge embedded within its parameters that can be potentially exploited for such applications. In this paper, we define the task of cultural adaptation and create an evaluation framework to evaluate the performance of modern LLMs for cultural adaptation and analyze their cross-cultural knowledge while connecting related concepts across different cultures. We also analyze possible issues with automatic adaptation. We hope that this task will offer more insight into the cultural understanding of LLMs and their creativity in cross-cultural scenarios.

EMNLP 2024

Translating Across Cultures: LLMs for Intralingual Cultural Adaptation

cross-cultural nlp

cultural adaptation

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

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

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EMNLP 2024 will take place in Miami, Florida from Nov 12th to Nov 16th, 2024, at the Hyatt Regency Miami Hote and on Underline for remote participants.

Although pre-trained language models (PLMs) are effective for natural language understanding (NLU) tasks, they demand a huge computational resource, thus preventing us from deploying them on edge devices. Researchers have therefore applied compression techniques for neural networks, such as pruning, quantization, and knowledge distillation, to the PLMs. Although these generic techniques can reduce the number of internal parameters of hidden layers in the PLMs, the embedding layers tied to the tokenizer are hard to compress, occupying a non-negligible portion of the compressed model. In this study, aiming to further compress PLMs reduced by the generic techniques, we exploit frequency-aware sparse coding to compress the embedding layers of the PLMs fine-tuned to downstream tasks. To minimize the impact of the compression on the accuracy, we retain the embeddings of common tokens as they are and use them to reconstruct embeddings of rare tokens by locally linear mapping. Experimental results on the GLUE and JGLUE benchmarks for language understanding in English and Japanese confirmed that our method can further compress the fine-tuned DistilBERT models models while maintaining accuracy.

Further Compressing Distilled Language Models via Frequency-aware Partial Sparse Coding of Embeddings

This study introduces Simple Reasoning with Code (SiRC), a novel instruction fine-tuning method for solving mathematical reasoning problems, particularly effective for Vietnamese, which is considered a low-resource language. Specifically, solving mathematical problems requires strategic and logical reasoning, which remains challenging in this research area. This paper presents a simple yet effective instruction fine-tuning method for mathematical reasoning. Unlike previous approaches, our proposed method effectively combines chain-of-thought reasoning with code transfer methods without requiring a sophisticated inference procedure. Furthermore, we focus on exploiting small open-source large language models (LLMs) for the Vietnamese language. In this regard, we first introduce a trainable Vietnamese math reasoning dataset, which is named ViMath-InstructCode. The proposed dataset is then used for fine-tuning open-source LLMs (e.g., less than 10 billion parameters). Experiments conducted on our custom ViMath-Ben

A Novel Instruction Tuning Method for Vietnamese Mathematical Reasoning using Trainable Open-Source Large Language Models

Large language models (LLMs) have achieved significant success in complex tasks across various domains, but they come with high computational costs and inference latency issues. Pruning, as an effective method, can significantly reduce inference costs. However, current pruning algorithms for encoder-based language models often focus on locally optimal solutions, neglecting a comprehensive exploration of the global solution space. This oversight can lead to instability in the solution process, thereby affecting the overall performance of the model. To address these challenges, we propose a structured pruning algorithm named G-Pruner (Global Pruner), comprising two integral components: PPOM (Proximal Policy Optimization Mask) and CG²MT (Conjugate Gradient Squared Mask Tuning), utilizing a global optimization strategy. This strategy not only eliminates the need for retraining but also ensures the algorithm's stability and adaptability to environmental changes, effectively addressing the issue of focusing solely

Global-Pruner: A Stable and Efficient Pruner for Retraining-Free Pruning of Encoder-Based Language Models

This paper presents the results of the patent translation shared task at the 11th Workshop on Asian Translation and 9th Conference on Machine Translation. Two teams participated in this task, and their submitted translation results for one or more of the six language directions were automatically and manually evaluated. The evaluation results demonstrate the strong performance of large language modelbased systems from both participants.

Results of the WAT/WMT 2024 Shared Task on Patent Translation

We present the joint CUNI and LMU submission to the MRL~2024 Shared Task on Multi-lingual Multi-task Information Retrieval. The shared task objective was to explore how we can deploy modern methods in NLP in multi-lingual low-resource settings, tested on two sub-tasks: Named-entity recognition and question answering. Our solutions to the subtasks are based on data acquisition and model adaptation. We compare the performance of our submitted systems with the translate-test approach which proved to be the most useful in the previous edition of the shared task. Our results show that using more data as well as fine-tuning recent multilingual pre-trained models leads to considerable improvements over the translate-test baseline. Our code is available at https://github.com/ufal/mrl2024-multilingual-ir-shared-task.

CUNI at WMT24 General Translation Task: LLMs, (Q)LoRA, CPO and Model Merging

This paper describes the development process of NMT systems that were submitted to the WMT~2024 General Translation and Biomedical shared tasks by the team of AIST AIRC. At WMT 2024 AIST AIRC participated in the General Machine Translation shared task and the Biomedical Translation task. We trained constrained track models for translation between English, German, and Japanese. Before training our models, we first filtered the parallel data, then performed iterative back-translation and additional filtering. We experimented with training baseline Transformer models, Mega models, and fine-tuning open-source T5 and Gemma model checkpoints using the filtered parallel data. Our primary submissions contain translations from ensembles of two Mega model checkpoints and our contrastive submissions are generated by our fine-tuned T5 model checkpoints.

AIST AIRC Systems for the WMT 2024 Shared Tasks

This poster describes the submission of the very first version of the Occiglot open-source large language model to the General MT Shared Task of the 9th Conference of Machine Translation (WMT24). Occiglot is an open-source, community-based LLM based on Mistral-7B, which went through language-specific continual pre-training and subsequent instruction tuning, including instructions relevant to machine translation. We examine the automatic metric scores for translating the WMT24 test set and provide a detailed linguistically-motivated analysis. Despite Occiglot performing worse than many of the other system submissions, we observe that it performs better than Mistral7B, which has been based upon, which indicates the positive effect of the language specific continual-pretraining and instruction tuning. We see the submission of this very early version of the model as a motivation to unite community forces and pursue future LLM research on the translation task.

Occiglot at WMT24: European Open-source Large Language Models Evaluated on Translation

This paper presents an evaluation of 16 machine translation systems submitted to the Shared Task of the 9th Conference of Machine Translation (WMT24) for the English-Hindi (en-hi) language pair using our Complex Structures Test (CoST) suite. Aligning with this year’s test suite sub- task theme, “Help us break LLMs”, we curated a comprehensive test suite encompassing diverse datasets across various categories, including autobiography, poetry, legal, conversation, play, narration, technical, and mixed genres. Our evaluation reveals that all the systems struggle significantly with the archaic style of text like legal and technical writings or text with creative twist like conversation and poetry datasets, highlighting their weaknesses in handling complex linguistic structures and stylistic nuances inherent in these text types. Our evaluation identifies the strengths and limitations of the submitted models, pointing to specific areas where further research and development are needed to enhance their performance.

CoST of breaking the LLMs

We assess the difficulty of gender resolution in literary-style dialogue settings and the influence of gender stereotypes. Instances of the test suite contain spoken dialogue interleaved with external meta-context about the characters and the manner of speaking. We find that character and manner stereotypes outside of the dialogue significantly impact the gender agreement of referents within the dialogue.

WMT24 Test Suite: Gender Resolution in Speaker-Listener Dialogue Roles

The GenderQueer Test Suite is designed to probe MT systems’ capabilities and limitations in handling gender agreement in morphologically rich languages such as Icelandic as well as non-binary pronouns and LGBTQIA+ terminology.

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