Summary of Pedal: Enhancing Greedy Decoding with Large Language Models Using Diverse Exemplars, by Sumanth Prabhu
PEDAL: Enhancing Greedy Decoding with Large Language Models using Diverse Exemplars
by Sumanth Prabhu
First submitted to arxiv on: 16 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 introduces PEDAL (Prompts based on Exemplar Diversity Aggregated using LLMs), a hybrid self-ensembling approach that combines diverse exemplars in prompts with Large Language Model (LLM) aggregation for text generation. Building on self-consistency techniques, which have shown remarkable performance gains with LLMs, PEDAL aims to achieve better accuracy and lower inference cost compared to existing methods like Greedy Decoding and Self Consistency. The authors leverage recent advancements in LLM inference, demonstrating that diverse exemplars can induce diversity in outputs. On publicly available datasets SVAMP and ARC, experiments reveal that PEDAL outperforms Greedy Decoding-based strategies while reducing inference costs compared to self-consistency approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper introduces a new way to generate text using machines called Large Language Models (LLMs). The method is called PEDAL, which combines two ideas: giving the machine diverse examples to work with and then combining its outputs. This approach can produce better results than existing methods while also being more efficient. The authors tested their idea on public datasets and found that it works well. |
Keywords
» Artificial intelligence » Inference » Large language model » Text generation
