Abstract:
Prompt reasoning methods often suffer from insufficient facts or factual errors when dealing with commonsense reasoning tasks. And in multi-step reasoning methods such as...View moreMetadata
Abstract:
Prompt reasoning methods often suffer from insufficient facts or factual errors when dealing with commonsense reasoning tasks. And in multi-step reasoning methods such as Chain-of-Thought, these errors can accumulate to the extent of yielding incorrect answers. Recent advancements in Tree-of-Thought reasoning aim to tackle the issue of error accumulation. Nevertheless, applying this approach to commonsense reasoning tasks proves challenging because these tasks resist easy decomposition in a standardized manner.We proposed ToFC, a prompt-based reasoning method for solving above problems in commonsense reasoning tasks. ToFC is a step-by-step, tree-like framework for prompt reasoning. It accomplishes stepwise reasoning and fact enhancement through continued Fact-Proposals; heuristically plans reasoning paths through Self-Evaluation; and balances accuracy and efficiency with a modified Best-First Search algorithm. ToFC tackles the issue of error accumulation in current prompt-based commonsense reasoning methods and addresses inefficiencies in the Tree-of-Thought framework. Experimental results indicate that ToFC outperforms existing prompt reasoning methods, and its extra overhead is within a reasonable range.
Date of Conference: 30 June 2024 - 05 July 2024
Date Added to IEEE Xplore: 09 September 2024
ISBN Information: