Social science NLP tasks, such as emotion or humor detection, are required to capture the semantics along with the implicit pragmatics from text, often with limited amounts of training data. Instruction tuning has been shown to improve the many capabilities of large language models (LLMs) such as commonsense reasoning, reading comprehension, and computer programming. However, little is known about the effectiveness of instruction tuning on the social domain where implicit pragmatic cues are often needed to be captured. We explore the use of instruction tuning for social science NLP tasks and introduce Socialite-Llama -- an open-source, instruction-tuned Llama. On a suite of 20 social science tasks, Socialite-Llama improves upon the performance of Llama as well as matches or improves upon the performance of a state-of-the-art, multi-task finetuned model on a majority of them. Further, Socialite-Llama also leads to improvement on 5 out of 6 related social tasks as compared to Llama, suggesting instruction tuning can lead to generalized social understanding. All resources including our code, model and dataset can be found through bit.ly/socialitellama.

社会科学领域的NLP任务需要捕捉文本中的语义和隐含的语用。我们探索了指令调整在社会科学NLP任务中的应用，并介绍了一个开源的、经过指令调整的Llama模型，即Socialite-Llama。在一系列社会科学任务中，Socialite-Llama在性能上优于Llama，并在大多数任务上与最先进的多任务微调模型相匹配或取得了更好的性能。此外，与Llama相比，Socialite-Llama还在6个相关的社会任务中有5个取得了改进，表明指令调整有助于广义的社会理解。我们的代码、模型和数据集等所有资源都可以在bit.ly/socialitellama找到。

SOCIALITE-LLAMA: 一种用于社会科学任务的指令调整模型