"InstructScore: Enhancing Explainability in Text Generation Evaluation

InstructScore: Enhancing Explainability in Text Generation Evaluation

The paper introduces InstructScore, a novel method for evaluating text generation that surpasses traditional models by providing detailed, explainable feedback instead of mere scores. This approach aims to offer deeper insights into the evaluation process, improving both transparency and utility.

Process Overview

The evaluation begins by generating a seed example using GPT-4, which is intentionally crafted to include errors. This error-laden data is then used to fine-tune a Llama model, adapting it to recognize and adjust for similar issues in future outputs.

Iterative Refinement and Feedback

Following fine-tuning, the Llama model is queried with specific questions that probe its understanding and handling of the input text. The responses from Llama undergo a rigorous evaluation process involving both automated tools and human reviewers. This stage assesses the alignment of the generated text with expected standards and outputs an alignment score.

Meta-Feedback for Continuous Improvement

The feedback, rich in specifics, is then fed back into the Llama model as part of a meta-feedback system. This iterative process not only refines the model’s performance but also enhances its ability to generate explanations for its text outputs.

Conclusion

InstructScore represents a significant advance in text generation evaluation by providing a framework that not only assesses textual outputs but also explains the basis of its evaluations. This method fosters greater understanding and trust in automated text generation systems, paving the way for more refined and accountable AI-driven content creation."

This version of the text provides a clearer and more structured overview of the InstructScore approach, emphasizing its iterative and explanatory nature, which sets it apart from traditional text evaluation models.


paper: https://arxiv.org/abs/2305.14282 

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