Ever since the Turing Test was proposed in the 1950s, humans have explored the mastering
of language intelligence by machine. Language is essentially a complex, intricate system of …

We study how vision-language models (VLMs) trained on web-scale data can be integrated
into end-to-end driving systems to boost generalization and enable interactivity with human …

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

Despite the impressive capabilities of Large Language Models (LLMs) on various tasks, they
still struggle with scenarios that involves complex reasoning and planning. Self-correction …

Benchmarks for large multimodal language models (MLMs) now serve to simultaneously
assess the general capabilities of models instead of evaluating for a specific capability. As a …

We explore the potential of self-play training for large language models (LLMs) in a two-
player adversarial language game called Adversarial Taboo. In this game, an attacker and a …

The recent development of chain-of-thought (CoT) decoding has enabled large language
models (LLMs) to generate explicit logical reasoning paths for complex problem-solving …

We introduce UFO, an innovative UI-Focused agent to fulfill user requests tailored to
applications on Windows OS, harnessing the capabilities of GPT-Vision. UFO employs a …

Large-scale cloud systems play a pivotal role in modern IT infrastructure. However, incidents
occurring within these systems can lead to service disruptions and adversely affect user …

This paper presents an advanced mathematical problem-solving framework, LLaMA-Berry,
for enhancing the mathematical reasoning ability of Large Language Models (LLMs). The …