Summary of Adaptive Reinforcement Learning Planning: Harnessing Large Language Models For Complex Information Extraction, by Zepeng Ding et al.
Adaptive Reinforcement Learning Planning: Harnessing Large Language Models for Complex Information Extraction
by Zepeng Ding, Ruiyang Ke, Wenhao Huang, Guochao Jiang, Yanda Li, Deqing Yang, Jiaqing Liang
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed two-stage multi-step method for large language models (LLMs) improves their performance in information extraction tasks by decomposing complex extraction tasks and executing them step-by-step. The extraction orders of entities significantly affect the final results, so an adaptive decision module is designed to provide the optimal order for sequential entity extraction on different sentences. This is achieved by modeling sequential extraction as a Markov decision process and training a decision model using the DDQN algorithm. Experimental results on multiple public datasets demonstrate the effectiveness of this approach in enhancing the information extraction capabilities of LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can solve information extraction tasks through multi-step planning, but their behavior is unstable on complex sentences. This paper proposes a way to improve these models by breaking down difficult tasks into smaller steps and doing them one at a time. The order in which entities are extracted matters, so the authors developed a system that figures out the best order for each sentence. They used a special kind of decision-making process called Markov decision processes and trained their model using an algorithm called DDQN. The results show that this approach helps language models do information extraction better. |
