Evaluation methods for LLMs
Kiruthika Subramani
Innovating AI for a Better Tomorrow | AI Engineer | Google Developer Expert | Author | IBM Dual Champion | 200+ Global AI Talks | Master's Student at MILA
Hey all, Welcome back for the sixth Episode of Cup of Coffee Series with LLMs. Again we have Mr. Bean with us.
Are you here for the first time ? Check out my first article where I discussed the LLMs intro and transformer architecture. And the second one where I discussed the first two steps involoved in building LLMs. And the third one where I discussed Model Architecture & Design of LLMs. And the fourth one where I discussed the pretraining and finetuning basics. And the fifth one where we discussed the fintuning methods of LLMs.
In this article, we are going to discuss different evaluation methods for LLMs.
Mr. Bean : Why we need to evaluate LLMs?
Evaluating Large Language Models (LLMs) is crucial to assess their performance, strengths, and weaknesses.
Let me explain you easily!!
Evaluation is like giving an LLM a report card. It helps us understand how well it's performing, identify areas for improvement, and ensure it's on the right track to be a valuable tool.
Come on Let's get started!!
Large Language Models (LLMs) have revolutionized how we interact with machines. However, assessing their effectiveness requires robust Text Processing Evaluation (TPE) methods.
Text Processing Evaluation (TPE) methods
Benchmarking
Benchmarking assess LLM performance. It utilizes pre-defined datasets designed for specific skills like question answering or summarization.
Imagine a benchmark dataset for question answering containing questions and corresponding human-written answers. The LLM is fed these questions, generating its own responses.
Metrics like accuracy (percentage of correct answers) then compare the LLM's outputs to the human-written references. This analysis reveals the LLM's strengths and weaknesses in question answering relative to other models benchmarked on the same dataset.
By comparing your LLM's score against published scores from other models, you gain valuable insights for improvement.
one example for it is,
Evaluating an LLM for machine translation. We would benchmark it against established translation benchmarks like BLEU score, comparing its translations to human-generated ones.
Human Evaluation
This method involves human experts assessing the LLM's outputs for factors like coherence, factual accuracy, and adherence to user instructions. This provides valuable insights into the user experience and overall effectiveness of the LLM.
one example for it is,
Evaluating an LLM for writing creative fiction. Human experts would assess the generated stories for originality, coherence, and adherence to specific genres or themes.
Perplexity
This measure evaluates how well the LLM predicts the next word in a sequence. A lower perplexity score indicates better predictive ability, signifying the LLM can effectively predict the upcoming words in a sequence, potentially leading to better fluency.
one example for it is,
Evaluating an LLM for generating realistic dialogue. Lower perplexity suggests the LLM can predict natural and coherent responses in a conversation.
LLM-as-a-Judge (LLM-Judge)
This method utilizes one LLM (Judge-LLM) to evaluate the outputs of another LLM (Target-LLM). This approach assesses factors like fluency, coherence, and adherence to specific criteria.
Real-World Example:
Evaluating an LLM for generating product descriptions. An LLM-Judge trained on high-quality product descriptions can assess the generated outputs for clarity, conciseness, and effectiveness.
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Choosing the Right Method
The choice of TPE method depends on the specific LLM application,
Tasks requiring high accuracy and factual correctness (healthcare or finance) necessitate benchmarking and human evaluation.
Tasks focusing on creativity and fluency (like writing poetry) benefit from LLM-Judge and perplexity.
A combination of methods is crucial for a well-rounded evaluation, ensuring the LLM is fit for its intended purpose.
Mr.Bean : what are the metrics used to asses llm's performance?
Some common metrics with formulas and explanations:
1. Accuracy (for classification tasks)
Measures the percentage of times the LLM makes correct predictions. For example, if an LLM tasked with classifying images as cats or dogs correctly identifies 80 out of 100 images, its accuracy is 80%.
2. F1-score (for imbalanced datasets)
Balances precision (ability to identify relevant examples) and recall (ability to find all relevant examples). It's particularly useful for tasks with imbalanced datasets, where one class might have significantly fewer examples.
3. Perplexity (for language generation tasks)
Measures how well the LLM predicts the next word in a sequence. Lower perplexity indicates better predictive ability, suggesting the LLM can generate more fluent and coherent text.
4. ROUGE score (for text summarization)
Measures the overlap between the generated summary and human-written reference summaries. Higher ROUGE scores indicate better summarization quality, with the LLM capturing the key points of the original text.
5. BLEU score (for machine translation)
Similar to ROUGE score, BLEU score compares n-gram overlap between the machine-translated text and human-generated translations. It also penalizes overly concise translations.
A single metric might not capture the entire picture. Combining multiple metrics often provides a more comprehensive understanding of LLM performance.
I found this article much useful - https://medium.com/data-science-at-microsoft/evaluating-llm-systems-metrics-challenges-and-best-practices-664ac25be7e5
For today, we have discussed the different evaluation methods of LLMs. Thanks for joining me today. Let us discuss more on our next discussion after 48 hours.
Bye Everyone, Stay Tuned.
With Efforts,
Kiruthika Subramani.