Summary of Unused Information in Token Probability Distribution Of Generative Llm: Improving Llm Reading Comprehension Through Calculation Of Expected Values, by Krystian Zawistowski
Unused information in token probability distribution of generative LLM: improving LLM reading comprehension through calculation of expected values
by Krystian Zawistowski
First submitted to arxiv on: 11 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 improving LLM text decoding is presented, which significantly enhances perceived quality. By manipulating token probabilities through greedy decoding and large temperature scaling of logits, the entropy of scores can be increased, leading to substantial performance improvements on the SummEval summary scoring dataset. This method outperforms GPT-4 on two metrics for Mixtral (37%-56% vs 20%-46%) and Mistral (13-28% vs 6-8%). Additionally, a positional bias effect is observed. Furthermore, a probability-based tree sampling algorithm is employed to examine all most probable generations for given prompts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research shows how to make language models better at understanding text. By changing the way we decode what’s written, we can improve how well the model reads and understands text. The experiment used a special dataset to test this method and found that it works really well. It even beats another popular language model on some metrics! This new approach could help us build more accurate language models in the future. |
Keywords
» Artificial intelligence » Gpt » Language model » Logits » Probability » Temperature » Token
