Rephrasing Invokes Better Generations for Large Language Models (2024)

Abstract: In the realm of emerging multitasking abilities of Large language models (LLMs), methodologies like prompt tuning enable low-cost adaptation to downstream tasks without retraining the model. However, automatic input pre-processing when LLMs are unavailable is currently under-studied. This paper proposes ReLLM (Rephrasing for LLMs), a method that automatically paraphrases input content for better output generations. ReLLM replaces low-frequency lexical items with their high-frequency counterparts. This substitution is particularly beneficial for low-resource language tasks that lack sufficient training data and resources. ReLLM is user-friendly and requires no additional LLM training. Experimental results in cross-lingual summarization, and natural language inference demonstrate the effectiveness of ReLLM.

## introduction large language models (llms, @xcite ) such as chatgpt foot_0 and llama-2 @xcite have exhibited their power in tackling various tasks by providing corresponding prompts. a prompt typically consists of two parts @xcite : instruction that describes the nature of the task, and input that describes the specific context of the task. one of the keys to the success of eliciting the desired information from llm is prompt tuning @xcite @xcite which often involves experimenting with different prompt structures, wording, or formatting. yet, this stream of works usually focus on the refinement of the instruction part @xcite and overlook the value of modifying input contents. to this end, we propose a novel method called rellm, which rephrases the input content to the same meaning while written in different expressions to improve the generation quality. specifically, rellm substitutes the low-frequency words in input with their high-frequency counterparts that represent the same meaning. such a methodology is inspired by the fact that replacing low-frequency words in the pre-training procedure can improve language models @xcite . by employing rellm, we can derive benefits, especially for low-resource languages where access to ample training data is limited. however, this raises another question -how do we define lowfrequency? since the pre-training data for llms are usually not publicly released -like chatgpt, it could be unusual to define the frequency. we surprisingly found that using word frequency statistics that are online available can empirically impressively work well for rellm. we conduct experiments on two different tasks, cross-lingual summarization @xcite and natural language inference @xcite . we found that rellm can invoke better generations on these tasks compared to unmodified inputs. for example, the gain for summarization is up to 2x bleu-4 points (summarize texts written in english to latgalian). our contributions are three-fold : ## experiments we choose chatgpt as the llm to complete the word substitution task due to its impressive performance across various tasks and domains. specifically, we use gpt-3.5-turbo. this is a chatgpt we evaluate rellm on two different tasks: cross-lingual summarization and natural language inference. the first task is multilingual and we intend to demonstrate that it is easier for llms to align high-frequency words to the words in other languages. on the other hand, the natural language inference task focuses on examining the impact of low-frequency words within the same language. ## conclusion in this paper, we introduce rellm, a method designed to rephrase the input part of a prompt. our approach involves the substitution of lowfrequency words in the input with their highfrequency counterparts. we experimentally demonstrate that the rephrased prompt yields improved results in eliciting the desired information from llms compared to the original prompt. importantly, the entire rephrasing process can be executed without accessing the weights and training data of llms. this capability proves particularly valuable in scenarios where only apis are available.