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

Training on high-quality synthetic data from strong language models (LMs) is a common
strategy to improve the reasoning performance of LMs. In this work, we revisit whether this …

The development of Artificial Intelligence Generated Content (AIGC) has been facilitated by
advancements in model algorithms, scalable foundation model architectures, and the …

We introduce SELF-DISCOVER, a general framework for LLMs to self-discover the task-
intrinsic reasoning structures to tackle complex reasoning problems that are challenging for …

Retrieval-Augmented Generation (RAG) has quickly grown into a pivotal paradigm in the
development of Large Language Models (LLMs). While much of the current research in this …

The emergence of specialized large language models (LLMs) has shown promise in
addressing complex tasks for materials science. Many LLMs, however, often struggle with …

This paper explores the role of Large Language Models (LLMs) in revolutionizing interactive
Machine Translation (MT), providing a comprehensive analysis across nine innovative …

Path planning is a fundamental scientific problem in robotics and autonomous navigation,
requiring the derivation of efficient routes from starting to destination points while avoiding …

Large language model (LLM) powered chatbots are primarily text-based today, and impose
a large interactional cognitive load, especially for exploratory or sensemaking tasks such as …

One of the fundamental research goals for explanation-based Natural Language Inference
(NLI) is to build models that can reason in complex domains through the generation of …