Summary of Interactive Dualchecker For Mitigating Hallucinations in Distilling Large Language Models, by Meiyun Wang et al.
Interactive DualChecker for Mitigating Hallucinations in Distilling Large Language Models
by Meiyun Wang, Masahiro Suzuki, Hiroki Sakaji, Kiyoshi Izumi
First submitted to arxiv on: 22 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Computers and Society (cs.CY)
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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 Large Language Models (LLMs) have revolutionized machine learning tasks by enabling few-shot in-context learning at reduced costs. However, these models can produce hallucinations in domains with incomplete knowledge. To address this challenge, we introduce DualChecker, a framework that mitigates hallucinations and improves teacher-student model performance during knowledge distillation. DualChecker features ContextAligner to ensure aligned human labeling standards and a dynamic checker system that enhances model interaction through re-prompts and borderline case identification. We evaluate DualChecker on a green innovation textual dataset with binary, multiclass, and token classification tasks, achieving up to 17% improvement in F1 score for teacher models and 10% for student models. Student models fine-tuned with LLM predictions perform comparably to those with actual data, even in challenging domains. Our framework, datasets, models, and code are publicly available. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores how computers can learn new things quickly without needing a lot of training data. The researchers created a special tool called DualChecker that helps computers understand what they’re being taught and improves their learning process. This tool is important because it reduces the chance of computers making mistakes or “hallucinating” when they don’t have all the information. The researchers tested DualChecker with a dataset about green innovation, which involves recognizing different types of text. They found that DualChecker worked better than previous methods, helping computers learn up to 17% more accurately. This means that computers can be taught to recognize certain words or phrases just as well as if they were given lots of training data. |
Keywords
» Artificial intelligence » Classification » F1 score » Few shot » Knowledge distillation » Machine learning » Student model » Token
