How do we compare LLMs? I posted this question to Chatty and here are the comparison metrics:
Comparing different Large Language Models (LLMs) requires a mix of quantitative benchmarks, qualitative evaluations, and real-world application tests. Here are some key categories of tests:
(GenAI Generated – https://chatgpt.com/share/67a899c4-713c-800c-a9f4-851a075ee4f8 )
1. Standardized Benchmark Tests
These are widely used tests to compare the performance of LLMs on various tasks.
a) General Language Understanding & Reasoning
- MMLU (Massive Multitask Language Understanding) – Tests knowledge across 57 subjects, including math, history, law, and medicine.
- BIG-bench (Beyond the Imitation Game) – Evaluates diverse tasks such as logical reasoning, ethics, and creative writing.
- HellaSwag – Measures common sense reasoning and sentence completion ability.
- DROP (Discrete Reasoning Over Paragraphs) – Evaluates complex reasoning over multi-sentence text.
b) Code Generation & Understanding
- HumanEval (OpenAI) – Evaluates a model’s ability to generate correct code snippets.
- MBPP (Multi-turn Benchmark for Programming Problems) – Tests coding ability with multi-step reasoning.
c) Mathematical Problem-Solving
- GSM8K – A benchmark for grade-school-level math word problems.
- MATH (Mathematical Problem Solving Dataset) – Evaluates high school math problem-solving.
d) Knowledge Retrieval & Fact-Checking
- TriviaQA – Measures fact recall and knowledge retrieval ability.
- TruthfulQA – Tests how often an LLM generates truthful versus misleading information.
e) Multilingual Capabilities
- XTREME (Cross-lingual Transfer Evaluation) – Measures how well an LLM can generalize across multiple languages.
- XGLUE (Cross-lingual General Language Understanding) – Evaluates multilingual performance across NLP tasks.
2. Human-Led Qualitative Evaluations
These involve human reviewers assessing the LLMs’ performance in real-world tasks.
a) Response Quality & Coherence
- Fluency & Readability – Is the response well-structured and grammatically correct?
- Coherence & Relevance – Does the response stay on topic and answer the question effectively?
- Logical Consistency – Does the response make sense within its context?
b) Creativity & Originality
- Creative Writing Tests – Generating poetry, short stories, or scripts to assess originality.
- Idea Generation – Comparing how different LLMs generate ideas for brainstorming tasks.
c) Bias & Ethical Evaluation
- Bias & Fairness Tests – Checking for biased responses in gender, race, or political topics.
- Toxicity Detection – Using tools like Perspective API to measure how often the LLM generates harmful content.
d) Adversarial Robustness
- Jailbreak & Prompt Injection Tests – Testing how easily an LLM can be tricked into providing harmful content.
- Hallucination Rate – Measuring how often the model generates incorrect or made-up information.
3. Real-World Application Tests
These assess how LLMs perform in practical use cases.
a) Conversational AI & Chatbots
- User Satisfaction Surveys – Collecting user feedback on chatbot effectiveness.
- Task Completion Rate – Measuring how often the LLM correctly resolves user queries.
b) Cybersecurity & Threat Intelligence
- Phishing Detection & Prevention – Evaluating the LLM’s ability to identify and respond to phishing attempts.
- Malicious Code Generation Prevention – Testing whether the LLM refuses to generate harmful code.
c) Education & Training
- Question Answering for Teaching Assistants – Evaluating accuracy and depth of answers for educational queries.
- Adaptive Learning Support – Checking if the model can provide personalized learning feedback.
d) Business Use Cases
- Summarization & Report Writing – Comparing how well LLMs condense long reports into meaningful summaries.
- Customer Support Automation – Testing how well LLMs handle real-world customer service queries.
4. Speed, Cost, & Efficiency Comparisons
- Inference Speed – How fast the model generates a response.
- Token Cost – How expensive the model is per query for API usage.
- Compute Requirements – How much GPU power is needed for deployment.
5. Security & Compliance Tests
- Data Privacy Assessment – Evaluating if the LLM stores or leaks sensitive data.
- Regulatory Compliance – Checking if the LLM aligns with GDPR, HIPAA, or other compliance standards.
Final Thoughts
No single test can fully capture the performance of an LLM. A well-rounded evaluation involves benchmark testing, human qualitative review, real-world application testing, and efficiency analysis.
Comparing and ranking the performance of over 30 AI models (LLMs), also known as the Artificial Analysis Test (AAT), involves many tests listed above, such as testing APIs. For AAT, “Quality means the accuracy and completeness of a model’s responses, assessed by benchmarks like MMLU and Chatbot Arena Elo. Speed means the performance of the model as hosted by a hosting provider. Speed can be divided into latency and output speed, and Artificial Analysis measures speed metrics with our custom benchmark”.
I will attempt to start a little project to develop a test script for Generative AI, run the script across various LLMs and present the results. The metric development will run across several posts, and finally, I will assemble them into one comprehensive test model. Then, this model will be tested against various LLMs, and results will be revealed for each LLM before comparison. Do keep returning for new updates, and your comments will be highly valued.
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