Summary of Breaking the Ceiling Of the Llm Community by Treating Token Generation As a Classification For Ensembling, By Yao-ching Yu et al.
Breaking the Ceiling of the LLM Community by Treating Token Generation as a Classification for Ensembling
by Yao-Ching Yu, Chun-Chih Kuo, Ziqi Ye, Yu-Cheng Chang, Yueh-Se Li
First submitted to arxiv on: 18 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 A novel approach to ensembling Large Language Models (LLMs) is proposed, which fully exploits token-level probability information to improve classification accuracy. By treating each token generation step as a classification task, this method prevents early incorrect tokens from snowballing into errors. The authors experiment with state-of-the-art LLMs on various benchmarks, including exams and mathematics, and observe that their approach breaks the existing performance ceiling. Additionally, they explore ensembling only key tokens, achieving better performance with lower latency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models can be used to get better results by combining the predictions of multiple models. This is usually done by choosing the best answer from a list of possibilities. However, this method doesn’t use all the information available about each word in the sentence. In this paper, scientists developed a new way to combine the predictions of Large Language Models that takes into account the probability of each individual word being correct or not. This approach allows them to avoid mistakes made early on that can make it harder to get the right answer later on. The researchers tested their method on several tasks and found that it performs better than previous methods. |
Keywords
» Artificial intelligence » Classification » Probability » Token
