Do large language models (LLMs) know the law? LLMs are increasingly being used to
augment legal practice, education, and research, yet their revolutionary potential is …

Automatic reviewing helps handle a large volume of papers, provides early feedback and
quality control, reduces bias, and allows the analysis of trends. We evaluate the alignment of …

Abstract Prompt ensembling of Large Language Model (LLM) generated category-specific
prompts has emerged as an effective method to enhance zero-shot recognition ability of …

With the rapid advancement of Large Language Models (LLMs), significant progress has
been made in multi-agent applications. However, the complexities in coordinating agents' …

Recent studies show that collaborating multiple large language model (LLM) powered
agents is a promising way for task solving. However, current approaches are constrained by …

Digital agents capable of automating complex computer tasks have attracted considerable
attention due to their immense potential to enhance human-computer interaction. However …

Zero-shot Chain-of-Thought (CoT) prompting emerges as a simple and effective strategy for
enhancing the performance of large language models (LLMs) in real-world reasoning tasks …

Large language models (LLMs) and large multimodal models (LMMs) have shown great
potential in automating complex tasks like web browsing and gaming. However, their ability …

We introduce Buffer of Thoughts (BoT), a novel and versatile thought-augmented reasoning
approach for enhancing accuracy, efficiency and robustness of large language models …

Fine-tuning large pre-trained language models with Evol-Instruct has achieved encouraging
results across a wide range of tasks. However, designing effective evolving methods for …