TIFIN I ndia at S em E val-2025: Harnessing Translation to Overcome Multilingual IR Challenges in Fact-Checked Claim Retrieval (2025)

Abstract: We address the challenge of retrieving previously fact-checked claims in mono-lingual and cross-lingual settings - a critical task given the global prevalence of disinformation. Our approach follows a two-stage strategy: a reliable baseline retrieval system using a fine-tuned embedding model and an LLM-based reranker. Our key contribution is demonstrating how LLM-based translation can overcome the hurdles of multilingual information retrieval. Additionally, we focus on ensuring that the bulk of the pipeline can be replicated on a consumer GPU. Our final integrated system achieved a success@10 score of 0.938 (~0.94) and 0.81025 on the monolingual and crosslingual test sets respectively.

## introduction misinformation poses serious risks to society worldwide, with the world economic forum ranking it among the most pressing global threats. social media acts as a key channel for false content, breaking down trust in institutions and dividing communities, an effect seen most clearly during political events. our work addresses this problem through semeval-2025 shared task 7, which focuses on developing multilingual retrieval systems that can identify previously fact-checked claims, from the curated multiclaim dataset, when given social media posts in various languages. success is measured simply: the system is considered effective if a relevant fact-checked claim appears among the top 10 retrieved results. ## related work research in fake news detection has evolved significantly over the past decade. early works @xcite introduced methods to assess information credibility on social media, and also explored multi-modal approaches combining textual, user, and network features. recent works @xcite focused on linguistic characteristics along with external taxonomies to devise novel detection mechanisms. fact verification systems represent a critical approach to combating misinformation. the pioneering work of thorne et al. @xcite introduced fever (fact extraction and verification), dataset and evaluation framework that has become a benchmark in the field. building on this foundation, augenstein et al. @xcite developed multi-domain fact-checking models that can transfer knowledge across different topics and domains. neural fact verification approaches have gained prominence, with nie et al. @xcite proposing the neural evidence retriever-aggregator (nera) framework, which integrates evidence retrieval with claim verification. transformer-based architectures have further enhanced verification capabilities, as demonstrated by wadden et al. @xcite with their kgat (knowledge graph attention network) model that leverages structured knowledge for improved reasoning. recent work by (lewis et al., 2020) introduced retrieval-augmented generation (rag) models that combine neural retrievers with language models to generate verifiable explanations. ## results our experimental evaluation revealed several key findings about the effectiveness of different retrieval approaches. as shown in notably, stella's built-in s2p prompt provided a meaningful boost, improving performance from 0.8159 to 0.8305 (+1.8%), demonstrating its effectiveness in enhancing query formulation. further, incorporating translated posts yielded a much larger improvement, increasing success@10 to 0.8837 (+6.8% relative to baseline), highlighting the value of cross-lingual augmentation. fine-tuning on translated posts further enhanced retrieval effectiveness, reaching a success@10 score of 0.9217. this represents an 11% relative improvement over the base stella model and an additional 4.3% gain over using translated posts alone. as shown in our final configuration ( ## conclusion and future work we developed a novel approach for retrieving factchecked claims across multiple languages using a multi-stage system that combines llm-based translation, embedding models improved through hard-negative mining, and an advanced reranking approach incorporating reciprocal rank fusion. our results on the task data set show that this integrated approach works well to find relevant information across language boundaries, achieving competitive performance. additionally , we show that llm-based re-ranking is not required from a performance standpoint as it offers minimal gains. our recommendation is to simply use translation along with a finetuned embedding model, as this combination captures most of the performance gains. future work includes testing generalizability on other datasets, applying these techniques to broader information retrieval problems, evaluating quantized embedding vectors for improved efficiency, and exploring llm-based translation approaches for low-resource languages.