PLUG : Leveraging Pivot Language in Cross-Lingual Instruction Tuning (2024)

Abstract: Instruction tuning has remarkably advanced large language models (LLMs) in understanding and responding to diverse human instructions. Despite the success in high-resource languages, its application in lower-resource ones faces challenges due to the imbalanced foundational abilities of LLMs across different languages, stemming from the uneven language distribution in their pre-training data. To tackle this issue, we propose pivot language guided generation (PLUG), an approach that utilizes a high-resource language, primarily English, as the pivot to enhance instruction tuning in lower-resource languages. It trains the model to first process instructions in the pivot language, and then produce responses in the target language. To evaluate our approach, we introduce a benchmark, X-AlpacaEval, of instructions in 4 languages (Chinese, Korean, Italian, and Spanish), each annotated by professional translators. Our approach demonstrates a significant improvement in the instruction-following abilities of LLMs by 29% on average, compared to directly responding in the target language alone. Further experiments validate the versatility of our approach by employing alternative pivot languages beyond English to assist languages where LLMs exhibit lower proficiency. Code and data are available at https://github.com/ytyz1307zzh/PLUG.

## introduction instruction tuning has emerged as a crucial step in the evolution of generic ai assistants built atop large language models (llms) @xcite . its fundamental principle involves fine-tuning llms to adhere to human instructions, thereby generating responses that are not only coherent but also aligned with the natural language directives. as a result, instruction-tuned † this work was done when zhihan and dong-ho were interns at snap. 1 code and data are available at https://github.com/ ytyz1307zzh/plug . ## pivot language guided generation instruction tuning includes training an llm on a set of instructions and their corresponding responses. in this work, we propose to utilize a pivot language, a language with more abundant resources and in which the llm demonstrates better proficiency, to facilitate the instruction tuning of the lower-resource target languages. let (x, y) be an example in the instruction tuning dataset, where x is the instruction and y is the response. (x p , y p ) represents its form in the pivot language, and (x t , y t ) denotes its form in the target language. traditionally, given an instruction in the target language, llms are trained to perform monolingual response generation (top half of figure 2 ), i.e., the model is trained to directly predict the corresponding target response, or p(y t |x t ). however, this learning strategy usually encounters difficulties due to the limited foundational capabilities of the llm in the target language, as it does not leverage the model's high proficiency in the pivot language. to lower the barrier of instruction tuning, plug trains the model to leverage the pivot language as the intermediary in the instruction-following process. specifically, as shown in the bottom half of figure 2 , given the target language instruction x t , we train the model to first generate the pivot instruction x p and the corresponding pivot response y p , both in the pivot language, before generating the final response y t in the target language. in other words, the model is trained to predict p([x p ; y p ; y t ] |x t ) in one single pass, where semicolon represents sequence concatenation. each component in the concatenated output starts with specific indicator tokens, such as english instruction or 中文回复 (response in chinese). such tokens are used to structure the generation, and act as separators for extracting the target response y t as the final output. please check appendix a for additional details on training prompts. plug reduces the difficulty of generating the target response as compared to monolingual response training, mainly because: such relative ease of following instructions with plug is demonstrated by the example in figure 2 , where plug follows a review-writing instruction better than the monolingual response baseline. ## evaluation settings in our experiments, we primarily use english as the pivot language. we consider 4 distinct target languages for evaluation, including chinese (zh), korean (ko), italian (it), and spanish (es). these target languages are less represented than english in the pre-training data of most llms, including the ones we test with ( §4.2). ## conclusion in this work, we introduced plug, a simple yet effective approach of utilizing a higher-resource pivot language to facilitate the instruction tuning of llms on lower-resource languages. extensive experiments on 4 distinct target languages confirmed the effectiveness of plug. notably, plug brought considerable enhancements to the response quality for open-ended instructions, when compared to the conventional strategy of monolingual response training. furthermore, languages besides english can also act as pivot languages, enhancing the instruction-following capabilities of llms in their relatively weaker languages. additionally, plug also led to a promising increase in the truthfulness and reasoning ability of llms.