Summary of Hgot: Hierarchical Graph Of Thoughts For Retrieval-augmented In-context Learning in Factuality Evaluation, by Yihao Fang et al.
HGOT: Hierarchical Graph of Thoughts for Retrieval-Augmented In-Context Learning in Factuality Evaluation
by Yihao Fang, Stephen W. Thomas, Xiaodan Zhu
First submitted to arxiv on: 14 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 In this paper, researchers tackle the issue of factuality and hallucinations in large language models (LLMs) by introducing the hierarchical graph of thoughts (HGOT). HGOT is a structured approach that utilizes the emergent planning capabilities of LLMs to retrieve pertinent passages during in-context learning. The framework refines self-consistency majority voting for answer selection, incorporating citation recall and precision metrics to assess the quality of thoughts. This methodology prioritizes answers based on the citation quality of their thoughts and proposes a scoring mechanism considering factors such as citation frequency, self-consistency confidence, and retrieval module ranking. Experiments show that HGOT outperforms competing models in FEVER by up to 7% and matches leading models in Open-SQuAD and HotPotQA. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem with really smart computers called large language models (LLMs). These computers can sometimes make things up instead of telling the truth. The researchers created something called the hierarchical graph of thoughts (HGOT) to help fix this issue. HGOT is like a map that helps the computer find the right answers and avoid making things up. It’s really good at doing this, and it even beats other methods in some tests. |
Keywords
» Artificial intelligence » Precision » Recall
