Summary of Efficient Exploration For Llms, by Vikranth Dwaracherla et al.
Efficient Exploration for LLMs
by Vikranth Dwaracherla, Seyed Mohammad Asghari, Botao Hao, Benjamin Van Roy
First submitted to arxiv on: 1 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Methodology (stat.ME); Machine Learning (stat.ML)
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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 how efficiently exploring human feedback can improve large language models. Researchers developed an agent that generates questions while learning from feedback received. The best-performing approach used double Thompson sampling, which represents uncertainty using a neural network. Results show that efficient exploration enables high performance with fewer queries. Uncertainty estimation and the choice of exploration scheme are crucial factors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how asking better questions can help improve large language models. A computer program was designed to ask questions while learning from people’s feedback. The best way to do this used a special technique called double Thompson sampling, which helps the agent understand its own uncertainty. By doing things more efficiently, the program could achieve high results with fewer questions. This matters because it can help improve how we interact with computers. |
Keywords
* Artificial intelligence * Neural network
