How can I evaluate a fine tuned LLM?

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Hi there! I am curious about how fine tuned large language models are evaluated? I mean, I fine tuned a model for specific task like a question-answer chatbot for my business but how can I decide to my language model is good, bad or terrible? Is there any ways to evaluate fine tuned model such as token similarity, semantic similarity…

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I’m not very familiar with it, but is it possible to use the evaluation method used in the leaderboard?

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Hi!

Evaluating a fine-tuned language model, especially for tasks like a question-answer chatbot, involves a mix of quantitative metrics and qualitative evaluation. Here are some common methods you can use to assess your model’s performance:

  1. Task-Specific Metrics:

    • Exact Match (EM): This is a simple metric that checks how often the model’s predicted answer matches the reference answer exactly. It’s commonly used for question-answering tasks.
    • F1 Score: This is more flexible than EM and measures the overlap between the predicted and reference answers. It’s especially useful if there are multiple valid answers.
    • Accuracy: The percentage of correct answers over the total number of questions asked. It’s a straightforward metric but can be limiting if the task involves complex or multiple possible answers.

  2. Semantic Similarity:

    • Cosine Similarity: This is often used to compare the predicted answer’s embedding (from models like BERT, GPT, etc.) with the reference answer’s embedding. Higher similarity indicates better performance.
    • BERTScore: BERTScore compares the similarity between the predicted and reference answers using embeddings from models like BERT. This can capture more nuanced differences in meaning compared to simple exact match scores.
    • BLEU Score: Typically used for machine translation, BLEU measures n-gram overlap and can be adapted for assessing similarity in question-answering tasks.

  3. Human Evaluation:
    You should follow @John6666 's advice.

  4. Real-World Testing:
    You should follow @John6666 's advice. This is very effective way to test your project using hugging face space. I’ve also used this space and this was helpful.

By combining these approaches, you can get a holistic view of your model’s quality and identify areas that need improvement. Task-specific metrics like F1 score and EM are great for initial evaluations, while human evaluation and real-world testing can offer deeper insights.

Hope this helps!

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Thanks a lot. I believe that semantic similarity will be good decision but I will try all of them.

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