RankPrompt: Step-by-Step Comparisons Make Language Models Better Reasoners
This addresses the issue of unreliable reasoning in LLMs for users in AI and NLP domains, offering a resource-efficient solution, though it is incremental as it builds on existing prompting techniques.
The paper tackled the problem of logical errors in large language models (LLMs) during reasoning tasks by introducing RankPrompt, a prompting method that enables LLMs to self-rank responses through step-by-step comparisons, resulting in performance improvements of up to 13% on reasoning tasks and 74% alignment with human judgments in evaluations.
Large Language Models (LLMs) have achieved impressive performance across various reasoning tasks. However, even state-of-the-art LLMs such as ChatGPT are prone to logical errors during their reasoning processes. Existing solutions, such as deploying task-specific verifiers or voting over multiple reasoning paths, either require extensive human annotations or fail in scenarios with inconsistent responses. To address these challenges, we introduce RankPrompt, a new prompting method that enables LLMs to self-rank their responses without additional resources. RankPrompt breaks down the ranking problem into a series of comparisons among diverse responses, leveraging the inherent capabilities of LLMs to generate chains of comparison as contextual exemplars. Our experiments across 11 arithmetic and commonsense reasoning tasks show that RankPrompt significantly enhances the reasoning performance of ChatGPT and GPT-4, with improvements of up to 13%. Moreover, RankPrompt excels in LLM-based automatic evaluations for open-ended tasks, aligning with human judgments 74% of the time in the AlpacaEval dataset. It also exhibits robustness to variations in response order and consistency. Collectively, our results validate RankPrompt as an effective method for eliciting high-quality feedback from language models.