Summary of Detecting Errors Through Ensembling Prompts (deep): An End-to-end Llm Framework For Detecting Factual Errors, by Alex Chandler et al.
Detecting Errors through Ensembling Prompts (DEEP): An End-to-End LLM Framework for Detecting Factual Errors
by Alex Chandler, Devesh Surve, Hui Su
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel large language model framework called Detecting Errors through Ensembling Prompts (DEEP) is proposed for detecting factual errors in text summarization. This end-to-end approach uses a diverse set of prompts to identify inconsistencies, treating outputs as binary features fed into ensembling models, and calibrates these models to produce accurate probabilities. DEEP achieves state-of-the-art balanced accuracy on three benchmarks: AggreFact-XSUM FTSOTA, TofuEval Summary-Level, and HaluEval Summarization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can summarize text accurately, but detecting errors is crucial. A new method called DEEP helps identify mistakes in summaries by using different prompts to find inconsistencies. This approach is better than previous methods at finding errors without requiring extra training or special settings. |
Keywords
» Artificial intelligence » Large language model » Summarization
