Large Language Models (LLMs) have recently demonstrated remarkable capabilities in
natural language processing tasks and beyond. This success of LLMs has led to a large …

A major factor in the recent success of large language models is the use of enormous and
ever-growing text datasets for unsupervised pre-training. However, naively training a model …

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

Large language models (LLMs), such as GPT-4, have shown remarkable performance in
natural language processing (NLP) tasks, including challenging mathematical reasoning …

Harnessing the power of human-annotated data through Supervised Fine-Tuning (SFT) is
pivotal for advancing Large Language Models (LLMs). In this paper, we delve into the …

In this paper, we present an innovative process-oriented math process reward model called
Math-shepherd, which assigns a reward score to each step of math problem solutions. The …

Large language models have made significant progress in various language tasks, yet they
still struggle with complex mathematics. In this paper, we propose ToRA a series of Tool …

We introduce MAmmoTH, a series of open-source large language models (LLMs)
specifically tailored for general math problem-solving. The MAmmoTH models are trained on …

Recent works like Tree-of-Thought (ToT) and Reasoning via Planning (RAP) aim to augment
the reasoning capabilities of LLMs by using tree-search algorithms to guide multi-step …

The development of emotion recognition in dialogue (ERC) has been consistently hindered
by the complexity of pipeline designs, leading to ERC models that often overfit to specific …