This survey paper proposes a clearer view of natural language reasoning in the field of
Natural Language Processing (NLP), both conceptually and practically. Conceptually, we …

Large language models (LLMs) have dramatically enhanced the field of language
intelligence, as demonstrably evidenced by their formidable empirical performance across a …

Language is essentially a complex, intricate system of human expressions governed by
grammatical rules. It poses a significant challenge to develop capable AI algorithms for …

Recent developments in large language models (LLMs) have been impressive. However,
these models sometimes show inconsistencies and problematic behavior, such as …

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 …

While large language models (LLMs) have demonstrated impressive performance on a
range of decision-making tasks, they rely on simple acting processes and fall short of broad …

Large Language Models (LLMs) are frequently used for multi-faceted language generation
and evaluation tasks that involve satisfying intricate user constraints or taking into account …

While GPT-4V (ision) impressively models both visual and textual information
simultaneously, it's hallucination behavior has not been systematically assessed. To bridge …

Large Language Models (LLMs) are increasingly being used for interactive decision-making
tasks requiring planning and adapting to the environment. Recent works employ LLMs-as …

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 …