The emergence of foundation models, such as large language models (LLMs) GPT-4 and
text-to-image models DALL-E, has opened up numerous possibilities across various …

With the rapid advancement of large language models (LLMs), information retrieval (IR)
systems, such as search engines and recommender systems, have undergone a significant …

Large language models (LLMs) have achieved remarkable success across a wide spectrum
of tasks; however, they still face limitations in scenarios that demand long-term planning and …

Abstract The utilization of Large Language Models (LLMs) in financial trading has primarily
been concentrated within the stock market, aiding in economic and financial decisions. Yet …

Theory of mind (ToM) reasoning involves understanding that others have intentions,
emotions, and thoughts, which is crucial for regulating one's reasoning. Although large …

Large language models (LLMs) require lengthy prompts as the input context to produce
output aligned with user intentions, a process that incurs extra costs during inference. In this …

While many studies prove more advanced LLMs perform better on tasks such as math and
coding, we notice that in cryptocurrency trading, stronger LLMs work worse than weaker …

The task of model editing becomes popular for correcting inaccurate or outdated parametric
knowledge in Large Language Models (LLMs). However, there are major limitations of state …

Abstract Large Language Models (LLMs) have achieved remarkable outcomes in various
natural language processing tasks. However, their application to the highly specialized field …

Recent advances in measuring hardness-wise properties of data guide language models in
sample selection within low-resource scenarios. However, class-specific properties are …