Large Language Models (LLMs) have shown significant progress in Open-Domain Question Answering (ODQA), yet most evaluations focus on English and assume locale-invariant answers across languages.This assumption neglects the cultural and regional variations that affect question understanding and answer, leading to biased evaluation in multilingual benchmarks.To address these limitations, we introduce XLQA, a novel benchmark explicitly designed for locale-sensitive multilingual ODQA.XLQA contains 3,000 English seed questions expanded to eight languages, with careful filtering for semantic consistency and human-verified annotations distinguishing locale-invariant and locale-sensitive cases.Our evaluation of five state-of-the-art multilingual LLMs reveals notable failures on localesensitive questions, exposing gaps between English and other languages due to a lack of locale-grounding knowledge.We provide a systematic framework and scalable methodology for assessing multilingual QA under diverse cultural contexts, offering a critical resource to advance the real-world applicability of multilingual ODQA systems.Our findings suggest that disparities in training data distribution contribute to differences in both linguistic competence and locale-awareness across models.

Recent advancements in large language models (LLMs) highlight their fluency in generating responses to diverse prompts. However, these models sometimes generate plausible yet incorrect “hallucinated” facts, undermining trust. A frequent but often overlooked cause of such errors is the use of poorly structured or vague prompts by users, leading LLMs to base responses on assumed rather than actual intentions. To mitigate hallucinations induced by these ill-formed prompts, we introduce Curative Prompt Refinement (CPR), a plug-and-play framework for curative prompt refinement that 1) cleans ill-formed prompts, and 2) generates additional informative task descriptions to align the intention of the user and the prompt using a fine-tuned small language model. When applied to language models, we discover that CPR significantly increases the quality of generation while also mitigating hallucination. Empirical studies show that prompts with CPR applied achieves over a 90 % win rate over the original prompts without any external knowledge.

Pre-trained language models (PLMs) learn gender bias from imbalances in human-written corpora. This bias leads to critical social issues when deploying PLMs in real-world scenarios. However, minimizing bias is limited by the trade-off due to the degradation of language modeling performance. It is particularly challenging to detach and remove biased representations in the embedding space because the learned linguistic knowledge entails bias. To address this problem, we propose a compensatory debiasing strategy to reduce gender bias while preserving linguistic knowledge. This strategy utilizes two types of sentences to distinguish biased knowledge: stereotype and non-stereotype sentences. We assign small angles and distances to pairs of representations of the two gender groups to mitigate bias for the stereotype sentences. At the same time, we maximize the agreement for the representations of the debiasing model and the original model to maintain linguistic knowledge for the non-stereotype sentences. To validate our approach, we measure the performance of the debiased model using the following evaluation metrics: SEAT, StereoSet, CrowS-Pairs, and GLUE. Our experimental results demonstrate that the model fine-tuned by our strategy has the lowest level of bias while retaining knowledge of PLMs.

Although various face-related tasks have significantly advanced in recent years, occlusion and extreme pose still impede the achievement of higher performance. Existing face rotation or de-occlusion methods only have emphasized the aspect of each problem. In addition, the lack of high-quality paired data remains an obstacle for both methods. In this work, we present a self-supervision strategy called Swap-R&R to overcome the lack of ground-truth in a fully unsupervised manner for joint face rotation and de-occlusion. To generate an input pair for self-supervision, we transfer the occlusion from a face in an image to an estimated 3D face and create a damaged face image, as if rotated from a different pose by rotating twice with the roughly de-occluded face. Furthermore, we propose Complete Face Recovery GAN (CFR-GAN) to restore the collapsed textures and disappeared occlusion areas by leveraging the structural and textural differences between two rendered images. Unlike previous works, which have selected occlusion-free images to obtain ground-truths, our approach does not require human intervention and paired data. We show that our proposed method can generate a de-occluded frontal face image from an occluded profile face image. Moreover, extensive experiments demonstrate that our approach can boost the performance of facial recognition and facial expression recognition. The code is publicly available1