MULTILINGUAL SETTINGS

Aligning LLMs for Multilingual Consistency in Enterprise Applications (2025.emnlp-industry)

Challenge:	Reward models (RMs) are primarily trained and evaluated in English and their capabilities in multilingual settings remain understudied.
Approach:	They construct a multilingual RM evaluation benchmark that tests the chat, safety, reasoning, and translation capabilities of RMs in 23 languages.
Outcome:	The proposed model performs better for high-resource languages and improves with translation quality.

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Amit Agarwal, Hansa Meghwani, Hitesh Laxmichand Patel, Tao Sheng, Sujith Ravi, Dan Roth

Generalising Multilingual Concept-to-Text NLG with Language Agnostic Delexicalisation (2021.acl-long)

Challenge:	Large language models (LLMs) remain unreliable for global enterprise applications due to performance gaps between high-resource and mid/low-resourced languages .
Approach:	They propose a batch-wise alignment strategy that aligns model outputs across languages . this method improves non-English accuracy by up to 23.9% without compromising English performance .
Outcome:	The proposed approach improves non-English accuracy by up to 23.9% without compromising English performance, model reasoning, or retrieval quality.

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Giulio Zhou, Gerasimos Lampouras

Text Generation

Challenge:	Concept-to-text Natural Language Generation requires a delexicalisation of the input, but this often requires that the input appears verbatim in the output text.
Approach:	They propose a method that uses multilingual pretrained embeddings to inflect words in their correct form during relexicalisation.
Outcome:	The proposed method outperforms monolingual models in concept-to-text and in low resource environments.

Cross-Lingual Transfer of Cultural Knowledge: An Asymmetric Phenomenon (2025.acl-short)

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Chen Zhang, Zhiyuan Liao, Yansong Feng

Cross-Lingual Transfer

Challenge:	Existing studies evaluate whether large language models handle global cultural diversity . however, mechanisms behind cultural knowledge acquisition remain unexplored .
Approach:	They propose an interpretable framework to study cultural knowledge transfer in large language models . they observe bidirectional cultural transfer between English and other high-resource languages .
Outcome:	The proposed framework ensures training data transparency and controls transfer effects.

MultiMUC: Multilingual Template Filling on MUC-4 (2024.eacl-long)

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William Gantt, Shabnam Behzad, Hannah An, Yunmo Chen, Aaron White, Benjamin Van Durme, Mahsa Yarmohammadi

On Evaluating and Mitigating Gender Biases in Multilingual Settings (2023.findings-acl)

Challenge:	We present multilingual parallel template filling datasets for MUCs . systems were required to extract one template per incident, containing details about perpetrators, victims, weapons used .
Approach:	They introduce MultiMUC, the first multilingual parallel corpus for template filling . they obtain automatic translations from a strong multilingual machine translation system .
Outcome:	The proposed dataset includes translations of the classic MUC-4 template filling benchmark into Arabic, Chinese, Farsi, Korean, and Russian.

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Aniket Vashishtha, Kabir Ahuja, Sunayana Sitaram

When Flores Bloomz Wrong: Cross-Direction Contamination in Machine Translation Evaluation (2026.eacl-short)

Challenge:	Existing benchmarks and resources for evaluating gender biases in multilingual settings are limited.
Approach:	They propose to extend DisCo to different Indian languages using human annotations to evaluate gender biases in multilingual models.
Outcome:	The proposed benchmarks and mitigation techniques are extended beyond English to evaluate gender biases in multilingual models.

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David Tan, Pinzhen Chen, Josef Van Genabith, Koel Dutta Chowdhury

The Model’s Language Matters: A Comparative Privacy Analysis of LLMs (2026.findings-eacl)

Challenge:	Large language models (LLMs) can be benchmark-contaminated, resulting in inflated scores that mask memorization as generalization.
Approach:	They use the FLORES-200 translation benchmark as a diagnostic to investigate cross-direction data contamination.
Outcome:	The proposed model can be cross-directional, boosting performance in unseen translation directions due to target-side memorization.

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Abhishek Kumar Mishra, Antoine Boutet, Lucas Magnana

Aurora-M: Open Source Continual Pre-training for Multilingual Language and Code (2025.coling-industry)

Challenge:	Large language models are increasingly deployed in multilingual settings that process sensitive data . prior privacy evaluations focused on English, but new research shows that language matters for privacy leakage .
Approach:	They quantify six corpus-level linguistic indicators and evaluate vulnerability under three attack families.
Outcome:	The results show that language matters for privacy leakage in large language models . Italian exhibits the strongest exposure, while English and French are more resilient .

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Taishi Nakamura, Mayank Mishra, Simone Tedeschi, Yekun Chai, Jason T. Stillerman, Felix Friedrich, Prateek Yadav, Tanmay Laud, Vu Minh Chien, Terry Yue Zhuo, Diganta Misra, Ben Bogin, Xuan-Son Vu, Marzena Karpinska, Arnav Varma Dantuluri, Wojciech Kusa, Tommaso Furlanello, Rio Yokota, Niklas Muennighoff, Suhas Pai, Tosin Adewumi, Veronika Laippala, Xiaozhe Yao, Adalberto Barbosa Junior, Aleksandr Drozd, Jordan Clive, Kshitij Gupta, Liangyu Chen, Qi Sun, Ken Tsui, Nour Moustafa-Fahmy, Nicolo Monti, Tai Dang, Ziyang Luo, Tien-Tung Bui, Roberto Navigli, Virendra Mehta, Matthew Blumberg, Victor May, Hiep Nguyen, Sampo Pyysalo

CLASP: Few-Shot Cross-Lingual Data Augmentation for Semantic Parsing (2022.aacl-short)

Challenge:	Pretrained language models are integral part of AI applications, but their high computational cost limits accessibility.
Approach:	They evaluate Aurora-M, a 15B parameter multilingual open-source model trained on English, Finnish, Hindi, Japanese, Vietnamese, and code.
Outcome:	The proposed model outperforms existing models on English, Finnish, Hindi, Japanese, Vietnamese, and code.

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Andy Rosenbaum, Saleh Soltan, Wael Hamza, Marco Damonte, Isabel Groves, Amir Saffari

The Impact of Positional Encodings on Multilingual Compression (2021.emnlp-main)

Challenge:	Large Language Models excel at a low-resource level given limited data, but are unsuitable for runtime systems which require low latency.
Approach:	They propose a method to augment training data for a model 40x smaller (500M parameters) they use Alexa to generate synthetic data from Alexa 20B to augment the training set .
Outcome:	The proposed method improves low-resource SP on two datasets in low-source settings.

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Vinit Ravishankar, Anders Søgaard

Beyond English: The Impact of Prompt Translation Strategies across Languages and Tasks in Multilingual LLMs (2025.findings-naacl)

Challenge:	Several modifications have been proposed to improve monolingual language models, but none of them result in better multilingual models.
Approach:	They propose to add positional encodings to token embeddings to preserve word-order information in a non-autoregressive setting.
Outcome:	The proposed modifications tend to improve monolingual models, but none improve multilingual models.

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Itai Mondshine, Tzuf Paz-Argaman, Reut Tsarfaty

Mind the Gap: Multilingual Divide in LLM Bias Detection and Reasoning (2026.acl-srw)

Challenge:	Current LLMs are primarily trained on English data but also include data from other languages.
Approach:	They propose to use a pre-translation strategy to translate a task prompt into English before inference . they use 'a modular entity' that could be translated into four different languages .
Outcome:	The proposed strategies are based on a set of pre-trained data across 35 languages covering both low and high-resource languages.

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Medha Hira, Prachi Goyal, Raj Maheshwari, Arnav Goel

Improved Zero-shot Neural Machine Translation via Ignoring Spurious Correlations (P19-1)

Challenge:	Large Language Models (LLMs) are increasingly deployed in multilingual settings . but most bias evaluation remains English-centric and ignores how bias manifests within reasoning .
Approach:	They evaluate large language models with supervised fine-tuning and preference optimization . they find that bias varies substantially across languages, with consistent degradation in non-English settings .
Outcome:	The proposed model improves in English, Dutch, Spanish, and Turkish using the MBBQ benchmark.

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Jiatao Gu, Yong Wang, Kyunghyun Cho, Victor O.K. Li

The Language Barrier: Dissecting Safety Challenges of LLMs in Multilingual Contexts (2024.findings-acl)

Challenge:	Existing approaches to train a multilingual NMT model for low-resource languages are lacking in terms of number of supervised examples.
Approach:	They propose to use decoder pre-training and back-translation to solve the degeneracy problem by analyzing spurious correlations between source and decoded sentences.
Outcome:	The proposed methods show significant improvement over the pivot-based approach on three challenging multilingual datasets.

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Lingfeng Shen, Weiting Tan, Sihao Chen, Yunmo Chen, Jingyu Zhang, Haoran Xu, Boyuan Zheng, Philipp Koehn, Daniel Khashabi

Multilingual Collaborative Defense for Large Language Models (2025.findings-emnlp)

Challenge:	Recent studies show that malicious prompt instructions could solicit objectionable content from LLMs.
Approach:	They compare how state-of-the-art LLMs respond to malicious prompts in different languages . they find that LLM's generate unsafe responses more often when a prompt is written in a lower-resource language .
Outcome:	The proposed model can generate unsafe responses more often when a malicious prompt is written in a lower-resource language, and less irrelevant responses when written in lower-source languages.

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Hongliang Li, Jinan Xu, Gengping Cui, Changhao Guan, Fengran Mo, Kaiyu Huang

MultiTACRED: A Multilingual Version of the TAC Relation Extraction Dataset (2023.acl-long)

Challenge:	Existing safeguards for Large Language Models are vulnerable to "jailbreaking" harmful queries.
Approach:	They propose a learning method that optimizes a continuous soft safety prompt automatically to facilitate multilingual safeguarding of LLMs.
Outcome:	The proposed method outperforms previous approaches in multilingual jailbreak defense while exhibiting strong cross-lingual generalization.

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Leonhard Hennig, Philippe Thomas, Sebastian Möller

Teaching LLMs to Abstain across Languages via Multilingual Feedback (2024.emnlp-main)

Challenge:	Relation extraction (RE) is a fundamental task in information extraction, but its extension to multilingual settings is hindered by the lack of supervised resources comparable in size to large English datasets.
Approach:	They propose a dataset to analyze relation extraction (RE) in multilingual settings . they find machine translation is a viable strategy to transfer RE instances .
Outcome:	The proposed dataset covers 12 typologically diverse languages from 9 language families and is compared with existing datasets.

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Shangbin Feng, Weijia Shi, Yike Wang, Wenxuan Ding, Orevaoghene Ahia, Shuyue Stella Li, Vidhisha Balachandran, Sunayana Sitaram, Yulia Tsvetkov

Multilingual Needle in a Haystack: Investigating Long-Context Behavior of Multilingual Large Language Models (2025.naacl-long)

Challenge:	Existing studies on LLM abstention focus on English, but they show that it can reduce the accuracy of the model by 20.5% .
Approach:	They propose to teach LLMs to abstain in the face of knowledge gaps by generating multiple feedback items in related languages.
Outcome:	Extensive experiments show that the proposed approach outperforms baselines and achieves 9.2% improvement for low-resource languages.

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Amey Hengle, Prasoon Bajpai, Soham Dan, Tanmoy Chakraborty

Neural Semi-Markov Conditional Random Fields for Robust Character-Based Part-of-Speech Tagging (N19-1)

Challenge:	Recent large language models demonstrate remarkable abilities in responding to queries in diverse languages, but their ability to handle long multilingual contexts is unexplored.
Approach:	They propose a multilingual Needle-in-a-Haystack (MLNeedle) test to assess a model's ability to retrieve relevant information from a collection of multilingual distractor texts.
Outcome:	The proposed model performance is the lowest when the needle is in a language outside the English language family and (ii) located in the middle of the input context.

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Apostolos Kemos, Heike Adel, Hinrich Schütze

Robustness in NLP

Challenge:	Character-level models of tokens are effective at dealing with within-token noise and out-of-vocabulary words.
Approach:	They propose to eliminate the need for tokenizers by using a character-level semi-Markov conditional random field that uses neural networks for its character and segment representations.
Outcome:	The proposed model outperforms state-of-the-art part-of speech taggers on a noisy English dataset.

Evaluation of Multilingual Image Captioning: How far can we get with CLIP models? (2025.findings-naacl)

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Goncalo Emanuel Cavaco Gomes, Chrysoula Zerva, Bruno Martins

Text Generation

Challenge:	Existing approaches to evaluate image captions are English-centric, despite improvements in the CLIPScore metric . however, there are no available benchmarks for multilingual captioning evaluation .
Approach:	They propose to use machine-translated and machine-repurposed datasets to evaluate CLIPScore variants in multilingual settings.
Outcome:	The proposed evaluation strategies are based on machine-translated and human judgements.

Can LLMs reason over extended multilingual contexts? Towards long-context evaluation beyond retrieval over haystacks (2026.eacl-long)

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Amey Hengle, Prasoon Bajpai, Soham Dan, Tanmoy Chakraborty

Accurate polyglot semantic parsing with DAG grammars (2020.findings-emnlp)

Challenge:	Existing multilingual long-context benchmarks are myopic and inherently limited, as successful recall alone does not indicate a model’s capacity to reason over extended contexts.
Approach:	They propose a new synthetic benchmark for multilingual long-context reasoning that includes bAbI-style tasks that test multi-hop inference, aggregation, and epistemic reasoning.
Outcome:	The proposed benchmarks are based on a multilingual long-context model and span seven languages.

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Federico Fancellu, Ákos Kádár, Ran Zhang, Afsaneh Fazly

Multimodality

Challenge:	Semantic parsers treat graphs as strings or trees, but there is no guarantee that the output is a well-formed graph.
Approach:	They propose a graph-aware sequence model that utilizes a DAG grammar to guide graph generation.
Outcome:	The proposed model outperforms string-based and DAG-grammar models by a large margin and can guarantee the well-formed graphs.

Processing Language Resources of Under-Resourced and Endangered Languages for the Generation of Augmentative Alternative Communication Boards (2020.lrec-1)

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Anne Ferger

Breaking Language Preference in Multilingual RAG via Language-Controllable Retrieval and Language-Agnostic Reasoning (2026.findings-acl)

Challenge:	Under-resourced and endangered or small languages yield problems for automatic processing and exploiting because of the small amount of available data.
Approach:	They propose an approach using enriched linguistic research data to create communication boards commonly used in alternative augmentative communication (AAC) using lexical analysis and rich annotation, the boards can be imported into various AAC software.
Outcome:	The proposed approach uses lexical analysis and rich annotations to create communication boards commonly used in alternative augmentative communication (AAC) The created boards can be imported into various AAC software and are available under the CC BY-NC-SA 4.0 (public) license.

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Wenshuai Huo, Xiaocheng Feng, Baohang Li, Chengpeng Fu, Yichong Huang, Hui Wang, Bing Qin

R2-MultiOmnia: Leading Multilingual Multimodal Reasoning via Self-Training (2025.acl-long)

Challenge:	Existing approaches to improve retrieval accuracy and generation quality of large language models suffer from language preference.
Approach:	They propose a framework that explicitly disentangles multilingual RAG into language-controllable retrieval and language-agnostic reasoning.
Outcome:	Experimental results show that the proposed approach outperforms baselines across multilingual benchmarks.

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Leonardo Ranaldi, Federico Ranaldi, Giulia Pucci

An Empirical Study of Multilingual Reasoning Distillation for Question Answering (2024.emnlp-main)

Challenge:	Recent studies have introduced eclectic strategies to enhance MLLMs’ reasoning capabilities, but they remain related to a single language.
Approach:	They propose a modular approach that instructs models to abstract key elements of the reasoning process and refine reasoning trajectories via self-correction.
Outcome:	The proposed approach improves multimodal reasoning, gets aligned performances among the languages approaching strong models and improves the model's performance.

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Patomporn Payoungkhamdee, Peerat Limkonchotiwat, Jinheon Baek, Potsawee Manakul, Can Udomcharoenchaikit, Ekapol Chuangsuwanich, Sarana Nutanong

IndoSafety: Culturally Grounded Safety for LLMs in Indonesian Languages (2025.emnlp-main)

Challenge:	Existing efforts to distill reasoning capabilities have focused mainly on English, leaving multilingual distillation underexplored.
Approach:	They propose a method that incorporates incorrect rationales as additional guidance to improve multilingual reasoning in large language models.
Outcome:	Empirical results show that d-CoT-nR significantly surpasses the baseline, improving accuracy in unseen languages and correctness in step-by-step reasoning.

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Muhammad Falensi Azmi, Muhammad Dehan Al Kautsar, Alfan Farizki Wicaksono, Fajri Koto

Challenge:	Existing safety standards are often based on direct translations from English, which overlook key aspects of local communication.
Approach:	They propose a high-quality, human-verified safety evaluation dataset tailored for the Indonesian context.
Outcome:	The proposed dataset covers formal and colloquial Indonesian, along with three major local languages: Javanese, Sundanese, and Minangkabau.

WikiUMLS: Aligning UMLS to Wikipedia via Cross-lingual Neural Ranking (2020.coling-main)

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Afshin Rahimi, Timothy Baldwin, Karin Verspoor

SHADES: Towards a Multilingual Assessment of Stereotypes in Large Language Models (2025.naacl-long)

Challenge:	Using a neural reranking model, we can match a UMLS concept with a Wikipedia page, enabling manual alignment with minimal effort.
Approach:	They propose a cross-lingual neural reranking model to match a UMLS concept with a Wikipedia page, which achieves a recall@1of 72%, a substantial improvement of 20% over word- and char-level BM25.
Outcome:	The proposed model achieves recall@1of 72%, 20% better than word- and char-level BM25, and will facilitate easier access to Wikipedia for health professionals, patients, and NLP systems, including in multilingual settings.

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Margaret Mitchell, Giuseppe Attanasio, Ioana Baldini, Miruna Clinciu, Jordan Clive, Pieter Delobelle, Manan Dey, Sil Hamilton, Timm Dill, Jad Doughman, Ritam Dutt, Avijit Ghosh, Jessica Zosa Forde, Carolin Holtermann, Lucie-Aimée Kaffee, Tanmay Laud, Anne Lauscher, Roberto L Lopez-Davila, Maraim Masoud, Nikita Nangia, Anaelia Ovalle, Giada Pistilli, Dragomir Radev, Beatrice Savoldi, Vipul Raheja, Jeremy Qin, Esther Ploeger, Arjun Subramonian, Kaustubh Dhole, Kaiser Sun, Amirbek Djanibekov, Jonibek Mansurov, Kayo Yin, Emilio Villa Cueva, Sagnik Mukherjee, Jerry Huang, Xudong Shen, Jay Gala, Hamdan Al-Ali, null Tair Djanibekov, Nurdaulet Mukhituly, Shangrui Nie, Shanya Sharma, Karolina Stanczak, Eliza Szczechla, Tiago Timponi Torrent, Deepak Tunuguntla, Marcelo Viridiano, Oskar Van Der Wal, Adina Yakefu, Aurélie Névéol, Mike Zhang, Sydney Zink, Zeerak Talat

Towards Fast Multilingual LLM Inference: Speculative Decoding and Specialized Drafters (2024.emnlp-main)

Challenge:	Large Language Models reproduce and exacerbate social biases present in training data, and resources to quantify this issue are limited.
Approach:	They propose a multilingual parallel dataset to examine culturally-specific stereotypes that may be learned by LLMs.
Outcome:	The proposed dataset includes stereotypes from 20 regions around the world and 16 languages, spanning multiple identity categories subject to discrimination worldwide.

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Euiin Yi, Taehyeon Kim, Hongseok Jeung, Du-Seong Chang, Se-Young Yun

When Models Lie, We Learn: Multilingual Span-Level Hallucination Detection with PsiloQA (2025.findings-emnlp)

Challenge:	Large language models (LLMs) have revolutionized natural language processing and are limited by high inference time in multilingual settings.
Approach:	They propose a training recipe for an assistant model in speculative decoding, which are leveraged to draft and-then its future tokens are verified by the target LLM.
Outcome:	The proposed model significantly speeds up inference time and out-of-domain speedup across various languages.

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Elisei Rykov, Kseniia Petrushina, Maksim Savkin, Valerii Olisov, Artem Vazhentsev, Kseniia Titova, Alexander Panchenko, Vasily Konovalov, Julia Belikova

Wino-X: Multilingual Winograd Schemas for Commonsense Reasoning and Coreference Resolution (2021.emnlp-main)

Challenge:	Existing hallucination detection benchmarks operate at the sequence level and are limited to English . Existing methods lacking fine-grained, multilingual supervision are limited in English based on the sequence .
Approach:	They propose a large-scale, multilingual dataset annotated with span-level hallucinations across 14 languages.
Outcome:	The proposed dataset annotated with span-level hallucinations across 14 languages is scalable and cost-efficient.

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Denis Emelin, Rico Sennrich

CausalAbstain: Enhancing Multilingual LLMs with Causal Reasoning for Trustworthy Abstention (2025.findings-acl)

Challenge:	Winograd schemas are well-established tools for evaluating coreference resolution and commonsense reasoning capabilities of computational models.
Approach:	They present a dataset of German, French, and Russian schemas aligned with their English counterparts.
Outcome:	The proposed model improves in English and German, while the model improve in other languages.

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Yuxi Sun, Aoqi Zuo, Wei Gao, Jing Ma

RC3: Regularized Contrastive Cross-lingual Cross-modal Pre-training (2023.findings-acl)

Challenge:	Existing methods to reduce hallucinations in large language models are inaccurate and inaccuracies in the generated feedback.
Approach:	They propose a method that helps LLMs determine whether to utilize multiple generated feedback responses and how to identify the most useful ones.
Outcome:	Extensive experiments show that the proposed method outperforms baselines on encyclopedic and commonsense knowledge QA tasks.

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Chulun Zhou, Yunlong Liang, Fandong Meng, Jinan Xu, Jinsong Su, Jie Zhou

MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application (2026.acl-long)

Challenge:	Existing V&L pre-training methods rely on strictly-aligned multilingual image-text pairs generated from English-centric datasets.
Approach:	They propose a regularized cross-lingual visual contrastive learning objective that constrains representation proximity of weakly-aligned multilingual image-text pairs.
Outcome:	The proposed model outperforms competing models with weak zero-shot capability on 5 multi-modal tasks across 6 languages.

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Xueqing Peng, Lingfei Qian, Yan Wang, Ruoyu Xiang, Yueru He, Yang Ren, Mingyang Jiang, Vincent Jim Zhang, Yuqing Guo, Jeff Zhao, Huan He, Yi Han, Yun Feng, Yuechen Jiang, Yupeng Cao, Haohang Li, Yangyang Yu, Xiaoyu Wang, Penglei Gao, Shengyuan Lin, Keyi Wang, Shanshan Yang, Yilun Zhao, Zhiwei Liu, Peng Lu, Jerry Huang, Suyuchen Wang, Triantafillos Papadopoulos, Polydoros Giannouris, Efstathia Soufleri, Nuo Chen, Zhiyang Deng, Heming Fu, Yijia Zhao, Mingquan Lin, Meikang Qiu, Kaleb E Smith, Arman Cohan, Xiao-Yang Liu, Jimin Huang, Guojun Xiong, Alejandro Lopez-Lira, Xi Chen, Junichi Tsujii, Jian-Yun Nie, Sophia Ananiadou, Qianqian Xie

Multimodality

Challenge:	Existing evaluations of LLMs in finance are text-only, monolingual, and largely saturated by current models.
Approach:	They propose a multilingual and multimodal benchmark for evaluating LLMs in real financial contexts.
Outcome:	The first expert-annotated multilingual and multimodal benchmark is released . it evaluates 21 leading LLMs and shows they perform better in multilingual settings .

EuroVerdict: A Multilingual Dataset for Verdict Generation Against Misinformation (2025.findings-acl)

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Daniel Russo, Fariba Sadeghi, Stefano Menini, Marco Guerini

BIASEDTALES-ML: A Multilingual Dataset for Analyzing Narrative Attribute Distributions in LLM-Generated Stories (2026.findings-acl)

Challenge:	a global issue that shapes public discourse shapes opinion and decision-making . many multilingual work has focused on claim verification rather than generating explanatory verdicts .
Approach:	They propose a multilingual dataset designed for verdict generation covering eight European languages.
Outcome:	The EuroVerdict dataset covers claims, manual verdicts, and supporting evidence . it is compared with other datasets in eight European languages .

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Yuxuan Ouyang, Yingfeng Luo, JingBo Zhu, Tong Xiao

Explain-then-Process: Using Grammar Prompting to Enhance Grammatical Acceptability Judgments (2025.findings-acl)

Challenge:	Existing studies on the use of Large Language Models (LLMs) focus primarily on English, leaving the cross-lingual generalization of aligned behavior underexplored.
Approach:	They propose a structured generator-extractor pipeline and a multi-dimensional distributional analysis framework to examine how narrative attributes vary across languages, models, and social conditions.
Outcome:	The proposed model reveals substantial cross-lingual variability in narrative generation patterns, indicating that distributions observed in English do not always exhibit similar characteristics in other languages, particularly in lower-resource settings.

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Russell Scheinberg, Ameeta Agrawal, Amber Shore, So Young Lee

Balanced Multi-Factor In-Context Learning for Multilingual Large Language Models (2025.emnlp-main)

Challenge:	Large language models (LLMs) can explain grammatical rules, but fail to apply those rules when judging sentence acceptability.
Approach:	They propose a grammar prompting paradigm that feeds an LLM's metalinguistic explanation back to the target model before deciding which sentence of a minimal pair is grammatical.
Outcome:	The proposed model improves on the English BLiMP, Chinese SLING, and Russian RuBLimp benchmarks.

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Masahiro Kaneko, Alham Fikri Aji, Timothy Baldwin

Towards the Roots of the Negation Problem: A Multilingual NLI Dataset and Model Scaling Analysis (2025.findings-emnlp)

Challenge:	Existing approaches address key factors that influence multilingual ICL, but they do not integrate them into the model.
Approach:	They propose a method that quantifies and optimally balances three factors for improved example selection.
Outcome:	Experiments on mCSQA and TYDI show that the proposed method outperforms existing methods.

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Tereza Vrabcová, Marek Kadlčík, Petr Sojka, Michal Štefánik, Michal Spiegel

Robustness in NLP

Challenge:	Negations are key to determining sentence meaning, making them essential for logical reasoning.
Approach:	They construct and publish two new textual entailment datasets in four languages with paired examples differing in negation.
Outcome:	The results show that increasing the model size may improve the models’ ability to handle negations.

FIND: Toward Multimodal Financial Reasoning and Question Answering for Indic Languages (2026.findings-acl)

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Sarmistha Das, Vaibhav Vishal, Syed Ibrahim Ahmad, Sriparna Saha, Manish Gupta

LangSAE Editing: Improving Multilingual Information Retrieval via Post-hoc Language Identity Removal (2026.acl-long)

Challenge:	Existing benchmarks for numerical reasoning in multilingual Indic languages are inadequate . e.g., FinVQA is a framework for evaluating financial numerical reasoning .
Approach:	They propose a framework that combines supervised fine-tuning with constraint-aware decoding to promote faithful numerical reasoning.
Outcome:	The proposed framework spans English, Hindi, Bengali, Marathi, Gujarati, and Tamil . it combines supervised fine-tuning with constraint-aware decoding to promote faithful numerical reasoning .

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Dongjun Kim, Jeongho Yoon, Chanjun Park, Heuiseok Lim

Model in Distress: Sentiment Analysis on French Synthetic Social Media (2026.findings-acl)

Challenge:	Existing methods for dense retrieval in multilingual environments encode language identity alongside semantics.
Approach:	They propose a method that trains on pooled embeddings to remove language-identity signal directly in vector space.
Outcome:	The proposed method improves ranking quality and cross-language coverage across multiple languages with especially strong gains for script-distinct languages.

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Pierre-Carl Langlais, Pavel Chizhov, Yannick Detrois, Carlos Rosas Hinostroza, Ivan P. Yamshchikov, Bastien Perroy