most citedInstruction Tuning Vs. In-Context Learning: Revisiting Large Language Models in Few-Shot Computational Social Science

1 citations · 1 across the 3 of their papers we have counts for

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cs.CL2025

Learn to Select: Exploring Label Distribution Divergence for In-Context Demonstration Selection in Text Classification

Ye Jiang, Taihang Wang, Youzheng Liu +3

In-context learning (ICL) for text classification, which uses a few input-label demonstrations to describe a task, has demonstrated impressive performance on large language models…

cs.CL2025

Team QUST at SemEval-2025 Task 10: Evaluating Large Language Models in Multiclass Multi-label Classification of News Entity Framing

Jiyan Liu, Youzheng Liu, Taihang Wang +3

This paper describes the participation of QUST_NLP in the SemEval-2025 Task 7. We propose a three-stage retrieval framework specifically designed for fact-checked claim retrieval.…

cs.CL2024★ 1 cited

Instruction Tuning Vs. In-Context Learning: Revisiting Large Language Models in Few-Shot Computational Social Science

Taihang Wang, Xiaoman Xu, Yimin Wang +1

Real-world applications of large language models (LLMs) in computational social science (CSS) tasks primarily depend on the effectiveness of instruction tuning (IT) or in-context l…