In this paper, we present the system proposed by our team OldJoe, for the 8th edition of the AVeriTeC shared task, as part of the FEVER workshop. The objective of this task is to verify the factuality of real-world claims. Our approach integrates open source large language models, SQL, and in-context learning. We begin with embedding the knowledge store using a pretrained embedding language model then storing the outputs in a SQL database. Subsequently, we prompt an LLM to craft relevant questions based on the input claim, which are then used to guide the retrieval process. We further prompt the LLM to generate answers to the questions and predict the veracity of the original claim. Our system scored 0.49 on the HU-METEOR AVeriTeC score on the dev set and 0.15 on the Ev2R recall on the test set. Due to the time constraint we were unable to conduct additional experiments or further hyperparameter tuning. As a result, we adopted this pipeline configuration centered on the Qwen3-14B-AWQ model as our final submission strategy. The full pipeline is available on GitHub: https://github.com/farahft/OldJoe

Emerging research on bias attribution and interpretability have revealed how tokens contribute to biased behavior in language models processing English texts. We build on this line of inquiry by adapting the information-theoretic bias attribution score metric for implementation on models handling agglutinative languages—particularly Filipino. We then demonstrate the effectiveness of our adapted method by using it on a purely Filipino model and on three multilingual models—one trained on languages worldwide and two on Southeast Asian data. Our results show that Filipino models are driven towards bias by words pertaining to people, objects, and relationships—entity-based themes that stand in contrast to the action-heavy nature of bias-contributing themes in English (i.e., criminal, sexual, and prosocial behaviors). These findings point to differences in how English and non-English models process inputs linked to sociodemographic groups and bias.

Bias studies on multilingual models confirm the presence of gender-related stereotypes in masked models processing languages with high NLP resources. We expand on this line of research by introducing Filipino CrowS-Pairs and Filipino WinoQueer: benchmarks that assess both sexist and anti-queer biases in pretrained language models (PLMs) handling texts in Filipino, a low-resource language from the Philippines. The benchmarks consist of 7,074 new challenge pairs resulting from our cultural adaptation of English bias evaluation datasets—a process that we document in detail to guide similar forthcoming efforts. We apply the Filipino benchmarks on masked and causal multilingual models, including those pretrained on Southeast Asian data, and find that they contain considerable amounts of bias. We also find that for multilingual models, the extent of bias learned for a particular language is influenced by how much pretraining data in that language a model was exposed to. Our benchmarks and insights can serve as a foundation for future work analyzing and mitigating bias in multilingual models.