Specialist or Generalist? Instruction Tuning for Specific NLP Tasks (2023)

Abstract: The potential of large language models (LLMs) to simultaneously perform a wide range of natural language processing (NLP) tasks has been the subject of extensive research. Although instruction tuning has proven to be a data-efficient method for transforming LLMs into such generalist models, their performance still lags behind specialist models trained exclusively for specific tasks. In this paper, we investigate whether incorporating broadcoverage generalist instruction tuning can contribute to building a specialist model. We hypothesize that its efficacy depends on task specificity and skill requirements. Our experiments assess four target tasks with distinct coverage levels, revealing that integrating generalist instruction tuning consistently enhances model performance when the task coverage is broad. The effect is particularly pronounced when the amount of task-specific training data is limited. Further investigation into three target tasks focusing on different capabilities demonstrates that generalist instruction tuning improves understanding and reasoning abilities. However, for tasks requiring factual knowledge, generalist data containing hallucinatory information may negatively affect the model’s performance. Overall, our work provides a systematic guide for developing specialist models with general instruction tuning.

## introduction the latest generation of large language models (llms), such as chatgpt @xcite and gpt4 @xcite , are often referred to as generalist models for their exceptional generalizability to perform various natural language processing (nlp) tasks. recent studies @xcite @xcite suggest that (1) the foundation of their superior performance (i.e., knowledge and capabilities) is predominantly acquired during large-scale unsupervised pre-training; and (2) instruction tuning @xcite @xcite is an incredibly data-efficient method for unleashing the power of llms to complete realistic nlp tasks. however, under rigorous evaluation, the performance of those instructionfollowing generalist models often falls short compared to traditional task-specific specialist models @xcite @xcite . recently, there has also been a growing trend towards developing specialist models using instruction tuning @xcite @xcite @xcite . in this paper, we study how to better harness the power of llm for specific nlp tasks using instruction tuning. our research is motivated by the existence of various broad-coverage general-purpose instruction-following datasets @xcite @xcite @xcite and their surprising efficiency for turning llms into capable instructionfollowing generalists. for instance, @xcite shows that merely one thousand supervised input-output pairs are necessary to build a competent generalist. in contrast to general-purpose instruction tuning, our preliminary experiments show that a sufficiently large set of task-specific data is still required for transforming an llm into a superior specialist. this leads us to a pivotal research question: how to better unleash the power of llms for specific nlp tasks by marrying the best of two worlds? more specifically, can generalpurpose instruction-following datasets aid in the transformation of an llm into a specialist? if so, when and how? we hypothesize the answers to the previous ques-tions depend on (1) how specific the target task is; and in sum, to the best of our knowledge, our work is the first effort to present a systematic guide for building and improving specialist models with general instruction tuning. ## background: instruction tuning in recent years, large language models (llms) have undergone rapid development and have dominated the field of natural language processing (nlp) @xcite @xcite . today's llms, such as chatgpt @xcite and @xcite , can perform complex and diverse tasks in the unified form of following natural language instructions. generally, these models are trained in three separate stages: (1) large-scale unsupervised pre-training on raw text; and (2) instruction tuning via supervised learning @xcite @xcite @xcite ; and ## conclusions in this study, we thoroughly investigated the interaction between specialist data and generalist data in the context of targeting specific nlp tasks. our findings consistently demonstrate that the addition of generalist data leads to performance improvement when the task coverage is broad. this highlights the potential benefits of incorporating generalist data, particularly when the availability of specialist data is limited. furthermore, we extensively examined the impact of integrating generalist data on the model's core capabilities. surprisingly, we observed that the inclusion of generalist data did not enhance the model's factuality. in fact, generalist data containing hallucinatory information can have a negative impact. on the other hand, our experiments also revealed that the introduction of generalist data has positive effects on the model's understanding and reasoning abilities. overall, our findings highlight the importance of leveraging generalist data to enhance the understanding and reasoning capabilities of nlp models, thereby enabling them to tackle various tasks more effectively. however, careful consideration should be given to the quality and reliability of the generalist data to avoid adverse effects on the model's factual knowledge.