United States

Despite advances in the multilingual capabilities of Large Language Models (LLMs) across diverse tasks, English remains the dominant language for LLM research and development. So, when working with a different language, this has led to the widespread practice of pre-translation, i.e., translating the task prompt into English before inference. Selective pre-translation, a more surgical approach, focuses on translating specific prompt components. However, its current use is sporagic and lacks a systematic research foundation. Consequently, the optimal pre-translation strategy for various multilingual settings and tasks remains unclear. In this work, we aim to uncover the optimal setup for pre-translation by systematically assessing its use. Specifically, we view the prompt as a modular entity, composed of four functional parts: instruction, context, examples, and output, either of which could be translated or not. We evaluate pre-translation strategies across 35 languages covering both low and high-resource languages, on various tasks including Question Answering (QA), Natural Language Inference (NLI), Named Entity Recognition (NER), and Abstractive Summarization. Our experiments show the impact of factors as similarity to English, translation quality and the size of pre-trained data, on the model performance with pre-translation. We suggest practical guidelines for choosing optimal strategies in various multilingual settings

NAACL 2025

Beyond English: The Impact of Prompt Translation Strategies across Languages and Tasks in Multilingual LLMs

prompting

multilinguality

evaluation

### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
**[Continue reading...](https://drive.google.com/file/d/1jX-qGhqVSZZCIrAnJaz798pflu5Irrdn/view?usp=sharing)**

*- Colin Cherry, Google, NAACL 2025 General Chair* 

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Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

poster

Recent studies indicate that leveraging off-the-shelf or fine-tuned retrievers, capable of retrieving relevant in-context examples tailored to the input query, enhances few-shot in-context learning of English. However, adapting these methods to other languages, especially low-resource ones, poses challenges due to the scarcity of cross-lingual retrievers and annotated data. Thus, we introduce XAMPLER: Cross-Lingual Example Retrieval, a method tailored to tackle the challenge of cross-lingual in-context learning using only annotated English data. XAMPLER first trains a retriever based on Glot500, a multilingual small language model, using positive and negative English examples constructed from the predictions of a multilingual large language model, i.e., MaLA500. Leveraging the cross-lingual capacity of the retriever, it can directly retrieve English examples as few-shot examples for in-context learning of target languages. Experiments on two multilingual text classification benchmarks, namely SIB200 with 176 languages and MasakhaNEWS with 16 languages, demonstrate that XAMPLER substantially improves the in-context learning performance across languages.

XAMPLER: Learning to Retrieve Cross-Lingual In-Context Examples

The massive amounts of web-mined parallel data often contain large amounts of noise. Semantic misalignment, as the primary source of the noise, poses a challenge for training machine translation systems. In this paper, we first introduce a process for simulating misalignment controlled by semantic similarity, which closely resembles misaligned sentences in real-world web-crawled corpora. Under our simulated misalignment noise settings, we quantitatively analyze its impact on machine translation and demonstrate the limited effectiveness of widely used pre-filters for noise detection. This underscores the necessity of more fine-grained ways to handle hard-to-detect misalignment noise. By analyzing the reliability of the model’s self-knowledge for distinguishing misaligned and clean data at the token level, we propose self-correction—an approach that gradually increases trust in the model’s self-knowledge to correct the supervision signal during training. Comprehensive experiments show that our method significantly improves translation performance both in the presence of simulated misalignment noise and when applied to real-world, noisy web-mined datasets, across a range of translation tasks.

How to Learn in a Noisy World? Self-Correcting the Real-World Data Noise in Machine Translation

Multimodal foundation models hold significant potential for automating radiology report generation, thereby assisting clinicians in diagnosing cardiac diseases. However, generated reports often suffer from serious factual inaccuracy. In this paper, we introduce a fact-aware multimodal retrieval-augmented pipeline in generating accurate radiology reports (FactMM-RAG). We first leverage RadGraph to mine factual report pairs, then integrate factual knowledge to train a universal multimodal retriever. Given a radiology image, our retriever can identify high-quality reference reports to augment multimodal foundation models, thus enhancing the factual completeness and correctness of report generation. Experiments on two benchmark datasets demonstrate that our multimodal retriever significantly outperforms other state-of-the-art retrievers on both language generation and radiology-specific metrics, up to 6.5% and 2% score in F1CheXbert and F1RadGraph. Further analysis indicates that employing our factually-informed training strategy imposes an effective supervision signal, without relying on explicit diagnostic label guidance, and successfully propagate fact-aware capabilities from the multimodal retriever to the multimodal foundation model in radiology report generation.

Fact-Aware Multimodal Retrieval Augmentation for Accurate Medical Radiology Report Generation

Multimodal machine translation (MMT) aims to leverage additional modalities to assist in language translation. With limited parallel data, current MMT systems rely heavily on monolingual English captioning data. These systems face three key issues: they often overlook that visual signals are unnecessary in many cases, they lack transparency in how visual information is used for disambiguation when needed, and they have yet to fully explore the potential of large-scale vision-language models (LVLMs) for MMT tasks. To address these issues, we propose the Detect, Disambiguate, and Translate (DeDiT) framework, the first reasoning-based framework for MMT leveraging LVLMs. DeDiT detects ambiguity in the input sentence, performs visual reasoning only when ambiguity is found, and generates the final translation. We implemented two versions of DeDiT: a prompting method for large proprietary LVLMs and a fine-tuning method for smaller LVLMs using synthetic data. Experiments on the Multi30K and CoMMuTE benchmarks show that DeDiT outperforms state-of-the-art models in disambiguation accuracy and translation quality. We also introduce an improved evaluation metric for disambiguation accuracy that enhances performance assessment and can be applied to proprietary models accessed via APIs.

Detect, Disambiguate, and Translate: On-Demand Visual Reasoning for Multimodal Machine Translation with Large Vision-Language Models

We describe Belief Tree Propagation (BTProp), a probabilistic framework for LLM hallucination detection. To judge the truth of a statement, BTProp generates a belief tree by recursively expanding the initial statement into a set of logically related claims, then reasoning globally about the relationships between these claims. BTProp works by constructing a probabilistic model of the LM itself: it reasons jointly about logical relationships between claims and relationships between claim probabilities and LM factuality judgments via probabilistic inference in a ''hidden Markov tree''. This method improves over state-of-the-art baselines by 3%-9% (evaluated by AUROC and AUC-PR) on multiple hallucination detection benchmarks.

A Probabilistic Framework for LLM Hallucination Detection via Belief Tree Propagation

Sketches are a natural and accessible medium for UI designers to conceptualize early-stage ideas. However, existing research on UI/UX automation often requires high-fidelity inputs like Figma designs or detailed screenshots, limiting accessibility and impeding efficient design iteration. To bridge this gap, we introduce Sketch2Code, a benchmark that evaluates state-of-the-art Vision Language Models (VLMs) on automating the conversion of rudimentary sketches into webpage prototypes. Beyond end-to-end benchmarking, Sketch2Code supports interactive agent evaluation that mimics real-world design workflows, where a VLM-based agent iteratively refines its generations by communicating with a simulated user, either passively receiving feedback instructions or proactively asking clarification questions. We comprehensively analyze ten commercial and open-source models, showing that Sketch2Code is challenging for existing VLMs; even the most capable models struggle to accurately interpret sketches and formulate effective questions that lead to steady improvement. Nevertheless, a user study with UI/UX experts reveals a significant preference for proactive question-asking over passive feedback reception, highlighting the need to develop more effective paradigms for multi-turn conversational assistants.

Sketch2Code: Evaluating Vision-Language Models for Interactive Web Design Prototyping

Claim verification is a long-standing and challenging task that demands not only high accuracy but also explainability and thoroughness of the verification process. This task becomes an emerging research issue in the era of large language models (LLMs) since real-world claims are often complex, featuring intricate semantic structures or obfuscated entities. Traditional approaches typically address this by decomposing claims into sub-claims and querying a knowledge base to resolve hidden or ambiguous entities. However, the absence of effective disambiguation strategies for these entities can compromise the entire verification process. To address these challenges, we propose Verify-in-the-Graph (VeGraph), a novel framework leveraging the reasoning and comprehension abilities of LLM agents. VeGraph operates in three phases: (1) Graph Representation - an input claim is decomposed into structured triplets, forming a graph-based representation that integrates both structured and unstructured information; (2) Entity Disambiguation -VeGraph iteratively interacts with the knowledge base to resolve ambiguous entities within the graph for deeper sub-claim verification; and (3) Verification - remaining triplets are verified to complete the fact-checking process. Experiments using Meta-Llama-3-70B (instruct version) show that VeGraph achieves competitive performance compared to baselines across benchmarks (HoVer and FEVEROUS), effectively addressing claim verification challenges. Our source code and data are available for further exploitation.

Verify-in-the-Graph: Entity Disambiguation Enhancement for Complex Claim Verification with Interactive Graph Representation

Large language models (LLMs), with demonstrated reasoning abilities across multiple domains, have been largely underexplored for time-series reasoning (TsR), which is ubiquitous in the real world. In this work, we propose TimerBed, the first comprehensive testbed for evaluating LLMs’ TsR performance. Specifically, TimerBed includes stratified reasoning patterns with real-world tasks, diverse combinations of LLMs and reasoning strategies, and various supervised models as comparison anchors. We perform extensive experiments with TimerBed, test multiple current beliefs, and observe the initial failures of LLMs in TsR, as evidenced by the ineffectiveness of zero shot (ZST) and performance degradation of few shot in-context learning (ICL). Further, we identify one possible root cause: the numerical modeling of data. To address this, we propose a prompt-based solution VL-Time, with visualization-modeled data and language-guided reasoning. Experimental results demonstrate that VL-Time enables multimodal LLMs to be non-trivial ZST and powerful ICL reasoners for time series, achieving about 140% average performance improvement and 99% average token costs reduction. TimerBed and VL-Time are available at https: //github.com/AdityaLab/DeepTime/.

A Picture is Worth A Thousand Numbers: Enabling LLMs Reason about Time Series via Visualization

Traditional approach for fact verification (FV) includes a three-step pipeline relying short evidence snippets and encoder-only inference models. More recent approaches leverage the multi-turn nature of LLMs to address FV as a step-by-step problem where questions inquiring additional context are generated until there is enough information. While these approaches have been tested for encyclopedic claims, exploration on domain-specific and realistic claims is missing. In this work, we apply an iterative LLM-based FV system on three medical fact-checking datasets, showcasing improvements in the final performance and the potential of step-by-step FV systems in real-world settings for domain-specific claims.

Step-by-Step Fact Verification System for Biomedical Claims

Vector representations have been pivotal in advancing natural language processing (NLP), with prior research focusing on embedding techniques for mathematical expressions using mathematically equivalent formulations. While effective, these approaches are constrained by the size and diversity of training data. In this work, we address these limitations by introducing E-Gen, a novel e-graph-based dataset generation scheme that synthesizes large and diverse mathematical expression datasets, surpassing prior methods in size and operator variety. Leveraging this dataset, we train embedding models using two strategies: (1) generating mathematically equivalent expressions, and (2) contrastive learning to explicitly group equivalent expressions. We evaluate these embeddings on both in-distribution and out-of-distribution mathematical language processing tasks, comparing them against prior methods. Finally, we demonstrate that our embedding-based approach outperforms state-of-the-art large language models (LLMs) on several tasks, underscoring the necessity of optimizing embedding methods for the mathematical data modality. The source code and datasets are available at https://github.com/MLPgroup/E-Gen.

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XAMPLER: Learning to Retrieve Cross-Lingual In-Context Examples

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Beyond English: The Impact of Prompt Translation Strategies across Languages and Tasks in Multilingual LLMs