Data annotation generally refers to the labeling or generating of raw data with relevant
information, which could be used for improving the efficacy of machine learning models. The …

Abstract Large Language Models (LLMs) have transformed natural language processing
tasks successfully. Yet, their large size and high computational needs pose challenges for …

Graph Neural Networks (GNNs) have evolved to understand graph structures through
recursive exchanges and aggregations among nodes. To enhance robustness, self …

Large language models (LLMs) have pushed the limits of natural language understanding
and exhibited excellent problem-solving ability. Despite the great success, most existing …

Mathematical reasoning is a challenging task for large language models (LLMs), while the
scaling relationship of it with respect to LLM capacity is under-explored. In this paper, we …

Large Language Models (LLMs) have demonstrated remarkable capabilities in important
tasks such as natural language understanding and language generation, and thus have the …

Large-scale task planning is a major challenge. Recent work exploits large language
models (LLMs) directly as a policy and shows surprisingly interesting results. This paper …

Large models based on the Transformer architecture play increasingly vital roles in artificial
intelligence, particularly within the realms of natural language processing (NLP) and …

Integrating free-text explanations to in-context learning of large language models (LLM) is
shown to elicit strong reasoning capabilities along with reasonable explanations. In this …

We introduce Lumos, a novel framework for training language agents that employs a unified
data format and a modular architecture based on open-source large language models …