Summary of I Don’t Know: Explicit Modeling Of Uncertainty with An [idk] Token, by Roi Cohen et al.
I Don’t Know: Explicit Modeling of Uncertainty with an [IDK] Token
by Roi Cohen, Konstantin Dobler, Eden Biran, Gerard de Melo
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 A novel calibration method is proposed to combat hallucinations in large language models, which emit unwanted and factually incorrect text. The approach adds an [IDK] token to the model’s vocabulary and shifts probability mass to this token for incorrect predictions, allowing the model to express uncertainty explicitly. This method is evaluated across multiple model architectures and factual downstream tasks, demonstrating a tradeoff between precision and recall. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are great at learning real-world knowledge, but they sometimes make mistakes by saying things that aren’t true. To fix this, researchers came up with an idea to add a special “I don’t know” token to the model’s vocabulary. This way, when the model is unsure about something, it can say so instead of making something up. The new method was tested on different models and tasks, and it worked well, allowing the model to express uncertainty while still being accurate most of the time. |
Keywords
» Artificial intelligence » Precision » Probability » Recall » Token
