Although large language models (LLMs) have demonstrated impressive ability in code
generation, they are still struggling to address the complicated intent provided by humans. It …

The increasing development of large language models (LLMs) in code generation has
drawn significant attention among researchers. To enhance LLM-based code generation …

Humans acquire their native languages by taking part in communicative interactions with
their caregivers. These interactions are meaningful, intentional, and situated in their …

Aspect-based sentiment analysis (ABSA) aims to predict sentiment orientations towards
textual aspects by extracting insights from user comments. While pretrained large language …

In recent advancements, large language models (LLMs) have demonstrated remarkable
capabilities in diverse tasks, primarily through interactive question-answering with humans …

Achieving coordination while avoiding suboptimal equilibria poses a significant challenge in
decentralized multi-agent reinforcement learning (MARL) systems operating under limited …

Abstract Large Language Models (LLMs) obtain their instruction-following ability through
instruction tuning. While the quality of instruction data is considered critical for a successful …

Recently, significant advancements have been made in Large Language Models (LLMs)
through the implementation of various alignment techniques. These techniques enable …

Recently, multimodal large language models (MLLMs) have demonstrated strong visual
understanding and decision-making capabilities, enabling the exploration of autonomously …

Large Language Models (LLMs) are widely used in many different domains, but because of
their limited interpretability, there are questions about how trustworthy they are in various …