Self-Para-Consistency: Improving Reasoning Tasks at Low Cost for Large Language Models (2024)

Abstract: Recently, the self-consistency decoding strategy has shown the ability to improve performance for complex reasoning tasks with large language models (LLMs). However, the costs may be high because the sampling process of the strategy generates some low-probability text, resulting in low-quality reasoning paths. As a consequence, it requires a relatively large sampling number to obtain good aggregation performance. In this paper, we propose an alternative strategy, self-para-consistency . It first generates multiple paraphrases for each test question, then generates reasoning paths for the original and all the paraphrased questions based on greedy decoding, and finally selects the most consistent answer. Since all the candidate paths have relatively high probabilities, the sampling number could be much smaller than the self-consistency strategy. Extensive experiments on complex reasoning datasets demonstrate the effectiveness of our method in reducing the sampling number.

## introduction recently, large language models (llms) like gpt-3 were considered to have an emergent ability of chain of thought (cot) prompting @xcite to perform multi-step reasoning @xcite , though the term "emergent" is still debated @xcite . while promising, the cots generated through greedy decoding may fall into local optima. to alleviate this problem, @xcite proposed to sample a diverse set of cots and then aggregate them through majority voting, as shown in figure 1(a) . however, the self-consistency strategy may encounter a key challenge in reality, which is the high cost due to the sampling process producing low-probability reasoning paths and therefore needing a relatively large number to achieve considerable performance. for example, @xcite and @xcite set the sampling number to 40, making the sampling process expensive, especially for complex reasoning tasks with long reasoning paths. to alleviate this problem, we need to achieve a better trade-off between the quality and the diversity of generated reasoning paths. in this paper, we propose an alternative strategy, referred to as self-para-consistency. its key idea is to retain the advantage of greedy decoding, which is likely to have a higher average probability than the sampling process. then the diversity comes from generating different paraphrases of the given question. the intuition of paraphrasing is that llms are shown to be sensitive to spurious features in the prompts @xcite , and therefore may generate different text in response to paraphrases in different expressions. as shown in figure 1 (b), the proposed self-paraconsistency strategy consists of three steps. we first prompt an llm to generate paraphrases and then generate the corresponding reasoning path for each paraphrase and the original question through greedy decoding. the last step is the same as the self-consistency strategy, which is to find the most consistent answer via majority voting. we conduct extensive experiments on 6 reasoning datasets, of which the results demonstrate effectiveness in reducing the sampling number. the contributions of this work are as follows: ## related work recently, cot prompting has demonstrated impressive performance @xcite but has two challenges. the first is the inconsistency between the reasoning path and the result. the second is the local optimality of the generated reasoning path through greedy decoding @xcite . to address the inconsistency problem, a line of work has employed program languages instead of natural language to depict the reasoning path, referred as program-of-thought (pot) prompting @xcite @xcite . to address the problem of local optimality, wang et al. proposed the self-consistency strategy based on cot prompting @xcite . the quality-diversity trade-off. due to the diversity of natural language, there is a quality-diversity trade-off in text generation systems @xcite . although tuning the temperature or other parameters of llms can switch on the quality-diversity trade-off curve, previous studies suggest another type of diversity improvement @xcite @xcite . by sampling a latent variable z, text generation models can further improve diversity while still using greedy decoding or beam search. our method is inspired by this work, as we suppose that different prompts can involve extra diversity while still keeping considerable quality through greedy decoding. ## conclusion in this paper, we propose the self-para-consistency strategy, which serves as an alternative to the selfconsistency strategy with lower costs. the proposed method first prompts the llm to generate multiple paraphrases sequentially, and then generate reasoning paths in parallel. the diversity comes from paraphrasing instead of nondeterministic decoding strategies. extensive experiments show the effectiveness of the proposed method.