Large language models (LLMs) demonstrate powerful capabilities, but they still face
challenges in practical applications, such as hallucinations, slow knowledge updates, and …

Large language models (LLMs) have demonstrated remarkable capabilities across a broad
spectrum of tasks. They have attracted significant attention and been deployed in numerous …

Recent AI-assistant agents, such as ChatGPT, predominantly rely on supervised fine-tuning
(SFT) with human annotations and reinforcement learning from human feedback (RLHF) to …

Agents capable of carrying out general tasks on a computer can improve efficiency and
productivity by automating repetitive tasks and assisting in complex problem-solving. Ideally …

Large language models (LLMs) are increasingly adopted for a variety of tasks with implicit
graphical structures, such as planning in robotics, multi-hop question answering or …

Prompting-based large language models (LLMs) are surprisingly powerful at generating
natural language reasoning steps or Chains-of-Thoughts (CoT) for multi-step question …

Retrieval-augmented in-context learning has emerged as a powerful approach for
addressing knowledge-intensive tasks using frozen language models (LM) and retrieval …

Most large language models (LLMs) are trained once and never updated; thus, they lack the
ability to dynamically adapt to our ever-changing world. In this work, we perform a detailed …

Large Language Models (LLMs) have made remarkable strides in various tasks. Whether
LLMs are competitive few-shot solvers for information extraction (IE) tasks, however, remains …

This paper introduces a simple yet effective query expansion approach, denoted as
query2doc, to improve both sparse and dense retrieval systems. The proposed method first …