Summary of Teaching a Language Model to Distinguish Between Similar Details Using a Small Adversarial Training Set, by Chris Achard
Teaching a Language Model to Distinguish Between Similar Details using a Small Adversarial Training Set
by Chris Achard
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel approach is proposed to improve the performance of language models on natural language tasks, specifically Natural Language Inference (NLI). The current state-of-the-art models can achieve high accuracy on NLI tasks but struggle when faced with manually created adversarial examples. To address this issue, a fine-tuning strategy is designed and tested on a small adversarial training set. The results show a significant increase in accuracy (+13%) on the adversarial test set while maintaining good performance on the original NLI task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Language models are super smart at understanding human language, but they can be tricked by fake examples made by humans. Researchers tried to fix this problem by training the model on special “tricky” examples that were designed to help it learn to tell similar words apart. It worked! The model got 13% better at recognizing tricky sentences and still did great on normal language tasks. |
Keywords
» Artificial intelligence » Fine tuning » Inference
