Summary of Instruction Tuning Vs. In-context Learning: Revisiting Large Language Models in Few-shot Computational Social Science, by Taihang Wang et al.
Instruction Tuning Vs. In-Context Learning: Revisiting Large Language Models in Few-Shot Computational Social Science
by Taihang Wang, Xiaoman Xu, Yimin Wang, Ye Jiang
First submitted to arxiv on: 23 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates the effectiveness of instruction tuning (IT) versus in-context learning (ICL) for fine-tuning large language models (LLMs) on computational social science (CSS) tasks. ICL, which learns from examples without explicit gradient updates, is found to consistently outperform IT in most CSS tasks. The study also explores the impact of increasing training samples on LLM performance and reveals that sample quality matters, as simply adding more data can sometimes result in a performance decline. Additionally, the paper compares three prompting strategies and finds ICL to be more effective than zero-shot and Chain-of-Thought (CoT) approaches. Overall, this research highlights the advantages of ICL in handling CSS tasks in few-shot settings and emphasizes the importance of optimizing sample quality and prompting strategies for LLM classification performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how to make language models better for social science tasks like understanding people’s behavior online. It compares two ways to fine-tune these models: one way is instruction tuning, which gives the model specific instructions, while the other way is in-context learning, which lets the model learn from examples without extra guidance. The study finds that in-context learning usually does a better job than instruction tuning for this type of task. It also explores what happens when you add more data to train the model and shows that it’s not just about having more data – the quality of that data matters too. Finally, the paper looks at different ways to ask questions to these language models and finds that in-context learning does a better job than some other approaches. |
Keywords
» Artificial intelligence » Classification » Few shot » Fine tuning » Instruction tuning » Prompting » Zero shot
