The prolific use of Large Language Models (LLMs) as an alternate knowledge base requires them to be factually consistent, necessitating both correctness and consistency traits for paraphrased queries. Recently, significant attempts have been made to benchmark datasets and metrics to evaluate LLMs for these traits. However, structural simplicity (subject-relation-object) and contemporary association in their query formulation limit the broader definition of factuality and consistency. In this study, we introduce TeCFaP, a novel Temporally Consistent Factuality Probe task to expand the consistent factuality probe in the temporal dimension. To this end, we propose TEMP-COFAC, a high-quality dataset of prefix-style English query paraphrases. Subsequently, we extend the definitions of existing metrics to represent consistent factuality across temporal dimension. We experiment with a diverse set of LLMs and find most of them performing poorly on TeCFaP. Next, we propose a novel solution CoTSeLF (Consistent-Time-Sensitive Learning Framework) combining multi-task instruction tuning (MT-IT) with consistent-time-sensitive reinforcement learning (CTSRL) to improve temporally consistent factuality in LLMs. Our experiments demonstrate the efficacy of CoTSeLF over several baselines.

In this paper, we present CMCLIP, a Code-Mixed Contrastive Linked Image Pre-trained model, an innovative extension of the widely recognized CLIP model. Our work adapts the CLIP framework to the code-mixed environment through a novel cross-lingual teacher training methodology. Building on the strengths of CLIP, we introduce the first code-mixed pre-trained text-and-vision model, CMCLIP, specifically designed for Hindi-English code-mixed multimodal language settings. The model is developed in two variants: CMCLIP-RB, based on ResNet, and CMCLIP-VX, based on ViT, both of which adapt the original CLIP model to suit code-mixed data. We also introduce a large, novel dataset called Parallel Hybrid Multimodal Code-mixed Hinglish (PHMCH), which forms the foundation for teacher training. The CMCLIP models are evaluated on various downstream tasks, including code-mixed Image-Text Retrieval (ITR) and classification tasks, such as humor and sarcasm detection, using a code-mixed meme dataset. Our experimental results demonstrate that CMCLIP outperforms existing models, such as M3P and multilingual-CLIP, establishing state-of-the-art performance for code-mixed multimodal tasks. We would also like to assert that although our data and frameworks are on Hindi-English code-mix, they can be extended to any other code-mixed language settings.