Summary of Improving Classification Performance with Human Feedback: Label a Few, We Label the Rest, by Natan Vidra et al.
Improving Classification Performance With Human Feedback: Label a few, we label the rest
by Natan Vidra, Thomas Clifford, Katherine Jijo, Eden Chung, Liang Zhang
First submitted to arxiv on: 17 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed research tackles the challenge of obtaining labeled data for training machine learning models, particularly in unstructured data environments. The approach focuses on few-shot and active learning, leveraging human feedback on a limited number of labeled examples to refine AI models. Large Language Models (LLMs) such as GPT-3.5, BERT, and SetFit are employed to analyze the efficacy of using a small number of labeled examples to improve model accuracy. The study benchmarks this approach on various datasets, including Financial Phrasebank, Banking, Craigslist, Trec, and Amazon Reviews, demonstrating that with just a few labeled examples, it is possible to surpass zero-shot LLMs and achieve enhanced text classification performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps us understand how AI models can get better when we give them small amounts of information that humans have already checked. The goal is to make AI more accurate by asking people for help on just a few examples, rather than needing millions of labeled data points. We use special language models like GPT-3.5 and BERT to test this idea and see if it works. |
Keywords
* Artificial intelligence * Active learning * Bert * Few shot * Gpt * Machine learning * Text classification * Zero shot
