The unwavering disparity in labeled resources between resource-rich languages and those considered low-resource remains a significant impediment for Large Language Models (LLMs). Recent strides in cross-lingual in-context learning (X-ICL), mainly through semantically aligned examples retrieved from multilingual pre-trained transformers, have shown promise in mitigating this issue. However, our investigation reveals that LLMs intrinsically reward in-language semantically aligned cross-lingual instances over direct cross-lingual semantic alignments, with a pronounced disparity in handling time–sensitive queries in the X-ICL setup. Such queries demand sound temporal reasoning ability from LLMs, yet the advancements have predominantly focused on English. This study aims to bridge this gap by improving temporal reasoning capabilities in low-resource languages. To this end, we introduce mTEMPREASON, a temporal reasoning dataset aimed at the varied degrees of low-resource languages and propose Cross-Lingual Time-Sensitive Semantic Alignment (CLiTSSA), a novel method to improve temporal reasoning in these contexts. To facilitate this, we construct an extension of mTEMPREASON comprising pairs of parallel cross–language temporal queries along with their anticipated in-language semantic similarity scores. Our empirical evidence underscores the superior performance of CLiTSSA compared to established baselines across three languages -- Romanian, German, and French, encompassing three temporal tasks and including a diverse set of four contemporaneous LLMs. This marks a significant step forward in addressing resource disparity in the context of temporal reasoning across languages.

The increasing acceptance of large language models (LLMs) as an alternative to knowledge sources marks a significant paradigm shift across various domains, including timesensitive fields such as law, healthcare, and finance.To fulfill this expanded role, LLMs must not only be factually accurate but also demonstrate consistency across temporal dimensions, necessitating robust temporal reasoning capabilities.Despite this critical requirement, efforts to ensure temporal consistency in LLMs remain scarce including noticeable absence of endeavors aimed at evaluating or augmenting LLMs across temporal references in time-sensitive inquiries.In this paper, we seek to address this gap by introducing a novel benchmark entitled temporal referential consistency, accompanied by a resource TEMP-ReCon designed to benchmark a wide range of both open-source and closedsource LLMs with various linguistic contexts characterized by differing resource richness (including English, French, and Romanian).The findings emphasis that LLMs do exhibit insufficient temporal referent consistency.To address this, we propose UnTRaP, a reasoning path alignment-based model that aims to enhance the temporal referential consistency of LLMs.Our empirical experiments substantiate the efficacy of UnTRaP compared to several baseline models.

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 prefixstyle 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.* We leverage GPT-4 to extend manually constructed initial set of base subject-relation pairs.* All of them are male with their ages ranging between 25-35 years.