Large Language Model (LLM) evaluation is essential for assessing the performance, reliability, and safety of AI models. The evaluation process can be categorized into three levels: Basic, Intermediate, and Advanced. Each level incorporates different methods and benchmarks to ensure the model meets desired requirements.
Measures how well the model predicts the next word in a sequence.
Lower perplexity indicates better performance.
This metric ensures responses are grammatically correct and contextually appropriate. It is evaluated using readability scores (e.g., Flesch-Kincaid).
This metric evaluates whether the model generates relevant and on-topic responses. Common methods include manual evaluation and BLEU/ROUGE scores for summarization and translation tasks.
This metric measures variation in generated responses to avoid repetitive or generic replies. It is evaluated using Distinct n-gram metrics (Distinct-1, Distinct-2).
This metric measures inference speed and response latency for real-time applications.
This level assesses reasoning, factual accuracy, and ethical considerations.:
Ensures factual correctness and minimizes misinformation.
Benchmarks: TruthfulQA, FactScore, QAGuard.
Evaluates if the model follows logical patterns.
Benchmarks: HELLASWAG, WinoGrande, AI2 Reasoning Challenge (ARC).
Analyzes gender, racial, and cultural biases in generated content.
Metrics: CEAT (Contextual Embedding Association Test), BiasNLI.
Benchmarks: BBQ, CrowS-Pairs.
Ensures the model does not produce harmful or offensive content.
Benchmarks: RealToxicityPrompts, ToxiGen, Perspective API.
Assesses performance on different NLP tasks:
Reading comprehension: SQuAD, DROP
Mathematical problem-solving: GSM8K
Code generation: HumanEval, MBPP
Medical & Legal knowledge: MedQA, CaseHOLD
This level focuses on cutting-edge training techniques for robust, efficient, and highly capable models.
Training large AI models on multiple GPUs or TPUs.
Techniques:
Data Parallelism: Splitting data across multiple GPUs or TPUs.
Model Parallelism: Distributing model layers across GPUs or TPUs.
Pipeline Parallelism: Processing different model layers in a sequence.
Using structured prompts to guide LLM responses.
Few-shot learning: Training models with minimal examples.
Training models incrementally without forgetting past knowledge.
Techniques:
Knowledge Retention using LoRA (Low-Rank Adaptation): Effective for maintaining prior knowledge during incremental training.
Episodic memory storage for chatbot applications: Helps chatbots retain contextual information over longer conversations.
Adversarial Training & Robustness
Enhancing model resilience against attacks and misleading prompts.
Techniques:
Adversarial data augmentation: Expanding the training dataset with adversarial examples to improve model robustness.
Model fine-tuning with adversarial examples: Adjusting model parameters using adversarial data to strengthen its defenses.
Multimodal models are trained to understand and generate text, images, and speech.
Example models:
CLIP: Designed for image-text alignment.
Whisper: Used for speech recognition and synthesis.
Flamingo: A vision-language model for understanding and generating both visual and textual information.
| Evaluation Level | Key Metrics & Methods | Benchmarks/Tools |
|---|---|---|
| Basic | Perplexity, Fluency, Readability | BLEU, ROUGE, Flesch-Kincaid |
| Basic | Response Speed & Diversity | Distinct-n, Inference Latency |
| Intermediate | Truthfulness & Fact-Checking | TruthfulQA, FactScore |
| Intermediate | Logical & Commonsense Reasoning | HELLASWAG, WinoGrande, ARC |
| Intermediate | Bias & Fairness | CEAT, BBQ, CrowS-Pairs |
| Intermediate | Safety & Toxicity | RealToxicityPrompts, ToxiGen |
| Advanced | Adversarial Robustness | AdvGLUE, Red Teaming |
| Advanced | Long-Context Understanding | LAMBADA, LongBench |
| Advanced | Explainability | LIME, SHAP |
| Advanced | Human Evaluation | RLHF, Likert Ratings |
| Advanced | Scalability & Cost | Model Distillation, Quantization |
