You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Description: Use Language Models (LLMs) to generate summarized reviews for each restaurant in a dataset. The goal is to leverage the power of LLMs to automatically generate concise and informative summaries that capture the essence of customer reviews.
Key Points to Address:
Dataset and Preprocessing: Clean the dataset and preprocess it in a way that will work for the llm
LLM Training: Outline the process of training a language model using the restaurant review dataset. Discuss the choice of LLM architecture (e.g., GPT, BERT) and any fine-tuning techniques that may be employed.
Review Summarization Approach: Use techniques such as extractive summarization or abstractive summarization and apply to restaurant reviews.
Evaluation Metrics: Define the metrics that will be used to evaluate the quality and effectiveness of the generated summaries. Consider metrics such as ROUGE (Recall-Oriented Understudy for Gisting Evaluation) or BLEU (Bilingual Evaluation Understudy) scores.
The text was updated successfully, but these errors were encountered:
Description: Use Language Models (LLMs) to generate summarized reviews for each restaurant in a dataset. The goal is to leverage the power of LLMs to automatically generate concise and informative summaries that capture the essence of customer reviews.
Key Points to Address:
The text was updated successfully, but these errors were encountered: