Summary of Enhancing Answer Reliability Through Inter-model Consensus Of Large Language Models, by Alireza Amiri-margavi et al.
Enhancing Answer Reliability Through Inter-Model Consensus of Large Language Models
by Alireza Amiri-Margavi, Iman Jebellat, Ehsan Jebellat, Seyed Pouyan Mousavi Davoudi
First submitted to arxiv on: 25 Nov 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 The proposed framework brings together multiple large language models to generate and answer complex statistical questions when definitive answers are unavailable. The study evaluates how inter-model consensus improves response reliability and question quality. Key findings show that Claude and GPT-4 produce well-structured, less ambiguous questions with higher inter-rater agreement, while Gemini and LLaMA exhibit greater variability and lower reliability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses big language models to help each other answer tricky math problems when there’s no clear answer. The researchers found that some models work better than others at coming up with good questions. They also discovered that when multiple models work together, the answers are more reliable and helpful for creating AI systems that can reason together. |
Keywords
» Artificial intelligence » Claude » Gemini » Gpt » Llama
