Summary of Refer: Improving Evaluation and Reasoning Through Hierarchy Of Models, by Yaswanth Narsupalli et al.
ReFeR: Improving Evaluation and Reasoning through Hierarchy of Models
by Yaswanth Narsupalli, Abhranil Chandra, Sreevatsa Muppirala, Manish Gupta, Pawan Goyal
First submitted to arxiv on: 16 Jul 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 introduces a novel framework called ReFeR for evaluating the quality of outputs generated by generative models, such as large language models (LLMs) and vision language models (VLMs). The traditional methods for evaluation rely on human assessments or automatic metrics that show low correlation with human judgment. This study leverages LLMs and VLMs themselves to create a 2-level hierarchy of models, which can evaluate both text and images without relying on extensive training data. The framework is tuned-free, efficient, and provides constructive feedback. The paper evaluates ReFeR across four diverse evaluation tasks, surpassing previous benchmarks in terms of accuracy. Additionally, the framework demonstrates superior collective reasoning abilities in reasoning tasks. Two variants of the framework are introduced: ReFeR-Turbo, optimized for accelerated performance, and ReFeR-Lite, offering a more cost-effective solution that is comparable to ReFeR-Turbo in terms of accuracy. The framework uses LLMs and VLMs themselves as evaluation metrics, which leverages their capabilities without requiring extensive training data. This approach improves the accuracy of evaluations and provides constructive feedback. The study also presents two variants of the framework: ReFeR-Turbo, optimized for accelerated performance, and ReFeR-Lite, offering a more cost-effective solution that is comparable to ReFeR-Turbo in terms of accuracy. The paper’s key contributions include introducing a novel framework called ReFeR for evaluating generative model outputs, demonstrating its effectiveness across four evaluation tasks, and presenting two variants of the framework with varying levels of efficiency and accuracy. The study highlights the potential of this framework for reasoning tasks, as well as its ability to provide constructive feedback. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to evaluate the quality of outputs from generative models like language and image models. These models are hard to test because they’re so good at producing realistic text or images that it’s tricky to know if they’re doing it correctly. The authors come up with a clever solution by using the models themselves as judges. They create a special framework called ReFeR that can evaluate both text and images without needing lots of training data. This makes it more efficient than other methods. The paper shows how well this framework works on four different tasks, and it does really well! The authors also show that this framework is good at doing reasoning tasks, which are hard because they require using the information from multiple sources to figure out what’s going on. They even come up with two versions of their framework: one that is fast but might not be as accurate, and another that takes a bit longer but is more precise. |
Keywords
* Artificial intelligence * Generative model
