  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.
