Summary of Evaluating Consistency and Reasoning Capabilities Of Large Language Models, by Yash Saxena et al.
Evaluating Consistency and Reasoning Capabilities of Large Language Models
by Yash Saxena, Sarthak Chopra, Arunendra Mani Tripathi
First submitted to arxiv on: 25 Apr 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 paper investigates the consistency and reasoning capabilities of Large Language Models (LLMs) from both public and proprietary sources, with a focus on their ability to generate accurate and coherent responses. The authors utilize the Boolq dataset, which includes questions, answers, and explanations, to evaluate the models’ performance. The findings reveal that proprietary models generally outperform public models in terms of consistency and reasoning capabilities. However, even when presented with basic general knowledge questions, none of the models achieved a score of 90% in both consistency and reasoning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is about how well computers can understand and explain things. It looks at special computer programs called Large Language Models (LLMs) that are good at answering questions and talking like humans. The researchers wanted to see if these programs could be trusted because sometimes they give wrong answers or don’t make sense. They used a special test with lots of questions, answers, and explanations to check how well the LLMs did. They found that some LLMs from private companies are better at getting things right than others from public sources. But even the best ones still didn’t do very well when asked simple questions. |
