Large language models (LLMs) have demonstrated remarkable multilingual abilities in various applications. Unfortunately, recent studies have discovered that there exist notable disparities in their performance across different languages. Understanding the underlying mechanisms behind such disparities is crucial ensuring equitable access to LLMs for a global user base. Therefore, this paper conducts a systematic investigation into the behaviors of LLMs across 27 different languages on 3 different scenarios, and reveals a Linguistic Map correlates with the richness of available resources and linguistic family relations. Specifically, high-resource languages within specific language family exhibit greater knowledge consistency and mutual information dissemination, while isolated or low-resource languages tend to remain marginalized. Our research sheds light on a deep understanding of LLM’s cross-language behavior, highlights the inherent biases in LLMs within multilingual environments and underscores the need to address these inequities.

Text embedding requires a highly efficient method for training domain-specific models on limited data, as general models trained on large corpora lack universal applicability in highly specific fields. Therefore, we have introduced VAEGPT-Sim, an innovative model for generating synonyms that combines a denoising variational autoencoder with a target-specific discriminator to generate synonymous sentences that closely resemble human language. Even when trained with completely unsupervised settings, it maintains a harmonious balance between semantic similarity and lexical diversity, as shown by a comprehensive evaluation metric system with the highest average scores compared to other generative models. When VAEGPT-Sim is utilized as a module for contrastive learning in text representation, it delivers state-of-the-art results in small-dataset training on STS benchmarks, surpassing ConSERT by 2.8 points. This approach optimizes the effectiveness of text representation despite a limited corpus, signifying an advancement in domain-specific embedding technology.

Stock volatility prediction is an important task in the financial industry. Recent multimodal methods have shown advanced results by combining text and audio information, such as earnings calls. However, these multimodal methods have faced two drawbacks. First, they often fail to yield reliable models and overfit the data due to their absorption of stochastic information from the stock market. Moreover, using multimodal models to predict stock volatility suffers from gender bias and lacks an efficient way to eliminate such bias. To address these aforementioned problems, we use adversarial training to generate perturbations that simulate the inherent stochasticity and bias, by creating areas resistant to random information around the input space to improve model robustness and fairness. Our comprehensive experiments on two real-world financial audio datasets reveal that this method exceeds the performance of current state-of-the-art solution. This confirms the value of adversarial training in reducing stochasticity and bias for stock volatility prediction tasks.