Large Language Models (LLMs) have emerged as a groundbreaking technology with their
unparalleled text generation capabilities across various applications. Nevertheless …

While large language models (LLMs) have shown remarkable effectiveness in various NLP
tasks, they are still prone to issues such as hallucination, unfaithful reasoning, and toxicity. A …

Large language models (LLMs) such as ChatGPT and GPT-4 have shown impressive
performance in complex reasoning tasks. However, it is difficult to know whether the models …

Language agents have achieved considerable performance on various complex tasks.
Despite the incessant exploration in this field, existing language agent systems still struggle …

Multi-agent systems driven by large language models (LLMs) have shown promising
abilities for solving complex tasks in a collaborative manner. This work considers a …

Recent progress in LLMs discussion suggests that multi-agent discussion improves the
reasoning abilities of LLMs. In this work, we reevaluate this claim through systematic …

When applied to question answering and other text generation tasks, language models
(LMs) may be queried generatively (by sampling answers from their output distribution) or …

A standard paradigm for sentiment analysis is to rely on a singular LLM and makes the
decision in a single round under the framework of in-context learning. This framework suffers …

While vertical thinking relies on logical and commonsense reasoning, lateral thinking
requires systems to defy commonsense associations and overwrite them through …

We envision'AI scientists' as systems capable of skeptical learning and reasoning that
empower biomedical research through collaborative agents that integrate machine learning …