Summary of Decoding with Limited Teacher Supervision Requires Understanding When to Trust the Teacher, by Hyunjong Ok et al.
Decoding with Limited Teacher Supervision Requires Understanding When to Trust the Teacher
by Hyunjong Ok, Jegwang Ryu, Jaeho Lee
First submitted to arxiv on: 26 Jun 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 This paper explores the problem of efficiently improving generative quality in small-scale large language models (LLMs) under limited supervision scenarios. Existing decoding algorithms that utilize LLM supervision often rely on unlimited access to LLM predictions, but this is not practical when there are restrictions on token generation. The authors develop an algorithm to aggregate LLM and LLM predictions on initial tokens, taking into account the confidence of small-scale LLMs. They adaptively overtrust or disregard LLM predictions based on this confidence. Experimental results show that this approach provides consistent improvements over conventional decoding strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how small language models can use bigger models to get better at generating text. Right now, we don’t know the best way to do this when we only have a few chances to ask for help from the bigger model. The researchers came up with an idea that takes into account how sure the smaller model is about its predictions. They tested their approach on many different models and datasets and found it worked better than other methods. |
Keywords
» Artificial intelligence » Token
