Home AI News Shepherd: Enhancing Language Models with Natural Feedback for Trustworthy Text Generation

Shepherd: Enhancing Language Models with Natural Feedback for Trustworthy Text Generation

Shepherd: Enhancing Language Models with Natural Feedback for Trustworthy Text Generation

**Introducing Shepherd: A Language Model for Evaluating AI-generated Content**

Large language models (LLMs) have made significant advancements in generating coherent and contextually relevant text. However, they often produce inaccurate and nonsensical results. To address this issue and make language models more trustworthy, researchers at Meta AI Research have developed Shepherd, a language model specifically optimized for evaluating the output of other models.

Shepherd aims to identify and address specific problems in generated text, such as factuality, logical flaws, coherence, and alignment. It provides natural language feedback that includes deep topic knowledge, concrete suggestions for improvement, and broad judgments and recommendations.

To train and improve Shepherd, the researchers curated a high-quality feedback dataset from two unique sets: community feedback from online forums and human-annotated input on generated text across various tasks. By training Shepherd on a mix of these datasets, they were able to surpass other models like ChatGPT on several downstream tasks.

Comparing Shepherd’s feedback to other models like Alpaca, SelFee, and ChatGPT, the researchers found that Shepherd’s criticisms were often preferred. Alpaca tends to provide inaccurate feedback by complementing every model answer, while SelFee often ignores model answers or immediately answers the query without offering feedback.

Although ChatGPT performs well in providing accurate feedback, Shepherd offers a more consistent evaluation across different circumstances.

In conclusion, Shepherd is a novel language model that provides comprehensive criticisms of AI-generated content, thereby improving its quality. The researchers demonstrate the effectiveness of Shepherd across various generating tasks and provide a top-notch feedback dataset for future research in this field.

For more details, you can check out the [research paper](https://arxiv.org/abs/2308.04592) and [Github repository](https://github.com/facebookresearch/Shepherd).

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**About the Author**

Aneesh Tickoo is a consulting intern at MarktechPost. He is currently pursuing his undergraduate degree in Data Science and Artificial Intelligence from the Indian Institute of Technology (IIT), Bhilai. Aneesh is passionate about machine learning and spends most of his time working on projects in image processing. He enjoys collaborating with others on interesting projects.

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