Summary of Quasi-random Multi-sample Inference For Large Language Models, by Aditya Parashar et al.
Quasi-random Multi-Sample Inference for Large Language Models
by Aditya Parashar, Aditya Vikram Singh, Avinash Amballa, Jinlin Lai, Benjamin Rozonoyer
First submitted to arxiv on: 9 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 investigates the potential of arithmetic sampling, a multi-sample decoding strategy, in large language models (LLMs). Arithmetic sampling implicitly defines an arithmetic code book, enabling efficient and parallelizable sequence generation. The authors contrast arithmetic sampling with ancestral sampling across two tasks: chain-of-thought reasoning and machine translation. Results show that arithmetic sampling produces more diverse samples, leading to improved performance on the GSM8K dataset (3-5% point increase in accuracy) and WMT19 machine translation tasks (0.45-0.89% point increment in COMET score). The study demonstrates that arithmetic sampling can be an effective technique for generating multiple sequences without significant computational overhead. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores a new way to generate text using large language models. It’s called “arithmetic sampling” and it allows computers to quickly create many different versions of text. The authors compared this method to another one called “ancestral sampling” on two tasks: making logical connections between ideas and translating text from one language to another. They found that arithmetic sampling creates more diverse texts, which leads to better results in both tasks. This new technique could be useful for computers to generate text quickly and efficiently. |
Keywords
» Artificial intelligence » Translation
