Just like software development, A large portion of GenAI development is about making choices. There will be many times where your will be given a task and you will need to find out which LLM is best suited for the particular use case. Assessing LLMs success goes beyond simply checking if they can complete a request. Evaluating LLM performance is a multifaceted endeavor, requiring a nuanced approach to ensure these models are reliable and effective.
The Metrics Toolbox:
- Coherence and Fluency: Evaluating the logical flow, grammatical correctness, and readability of the generated text.
- Relevance and Context: Assessing the model's ability to generate contextually relevant and appropriate responses.
- Factual Accuracy: Measuring the truthfulness and consistency of the information generated by the LLM, especially for tasks like question-answering and knowledge generation.
- Fairness: Assessing the model's performance across different domains, languages, and demographic groups to ensure fairness and lack of bias.
Understanding of model architecture can also help understand which model to use. For instance, a Mixture of Expert(MOE) model has sub-models and a master model. These kind of model architecture can be a jack of all trades, but if you want a mathematics expert or coding expert you need to dig deeper into their architecture and find one. One more thing which can help you in making this decison of which llm to use is to go through some public benchmark results. Benchmarking is standardized tests that gauge LLM performance across various capabilities.
- Question Answering Benchmarks (e.g., SQuAD): These assess how well LLMs can answer questions posed over a specific dataset of text and answer pairs.
- Text Summarization Benchmarks (e.g., CNN/Daily Mail): These evaluate the ability of LLMs to condense lengthy passages into concise and informative summaries.
- Code Generation Benchmarks (e.g., HumanEval): These assess how well LLMs can generate functional computer code based on natural language descriptions.
- Reasoning and Commonsense Benchmarks (e.g., HellaSwag): These evaluate the model's ability to understand logical relationships and apply common sense reasoning in complex scenarios.
