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Natural language understanding (NLU) is integral to various social mediaapplications. However, existing NLU models rely heavily on context for semanticlearning, resulting in compromised performance when faced with short and noisysocial media content. To address this issue, we leverage in-context learning(ICL), wherein language models learn to make inferences by conditioning on ahandful of demonstrations to enrich the context and propose a novelhashtag-driven in-context learning (HICL) framework. Concretely, we pre-train amodel #Encoder, which employs #hashtags (user-annotated topic labels) to driveBERT-based pre-training through contrastive learning. Our objective here is toenable #Encoder to gain the ability to incorporate topic-related semanticinformation, which allows it to retrieve topic-related posts to enrich contextsand enhance social media NLU with noisy contexts. To further integrate theretrieved context with the source text, we employ a gradient-based method toidentify trigger terms useful in fusing information from both sources. Forempirical studies, we collected 45M tweets to set up an in-context NLUbenchmark, and the experimental results on seven downstream tasks show thatHICL substantially advances the previous state-of-the-art results. Furthermore,we conducted extensive analyzes and found that: (1) combining source input witha top-retrieved post from #Encoder is more effective than using semanticallysimilar posts; (2) trigger words can largely benefit in merging context fromthe source and retrieved posts.

HICL: Hashtag-Driven In-Context Learning for Social Media Natural Language Understanding