Large Language Models (LLMs) still struggle with complex reasoning tasks. Motivated by
the society of minds (Minsky, 1988), we propose ReConcile, a multi-model multi-agent …

Large language models (LLMs) have demonstrated remarkable capabilities in language
generation, understanding, and few-shot learning in recent years. An extensive body of work …

Modern large language models (LLMs) like ChatGPT have shown remarkable performance
on general language tasks but still struggle on complex reasoning tasks, which drives the …

Large Language Models (LLMs) are evolving at an unprecedented pace and have exhibited
considerable capability in the realm of natural language processing (NLP) with world …

While large language models (LLMs) are proficient at question-answering (QA), it is not
always clear how (or even if) an answer follows from their latent" beliefs". This lack of …

Abstract Large Language Models (LLMs) have recently made significant strides in complex
reasoning tasks through the Chain-of-Thought technique. Despite this progress, their …

We explore testing the reasoning ability of large language models (LLMs), such as
ChatGPT, by engaging with them in a debate-like conversation that probes deeper into their …

We investigate the extent to which contemporary Large Language Models (LLMs) can
engage in exploration, a core capability in reinforcement learning and decision making. We …

Recent advancements in large language models (LLMs) have shown potential for human-
like agents. To help these agents adapt to new tasks without extensive human supervision …

Multi-hop reasoning has been widely studied in recent years to obtain more interpretable
link prediction. However, we find in experiments that many paths given by these models are …