AI‑modelevaluatie: gids voor metriek en benchmarks

Grondige evaluatie is cruciaal voor de juiste modelkeuze. In deze gids bekijken we de metriek en benchmarks die worden gebruikt bij het evalueren van AI‑modellen.

Basismetriek

Perplexity

Meet hoe goed het taalmodel tekst voorspelt:

1import torch
2import math
3
4def calculate_perplexity(model, tokenizer, text):
5    encodings = tokenizer(text, return_tensors="pt")
6    
7    with torch.no_grad():
8        outputs = model(**encodings, labels=encodings["input_ids"])
9        loss = outputs.loss
10    
11    perplexity = math.exp(loss.item())
12    return perplexity
13
14# Low perplexity = Better model
15# Typical values: 5-20 (good), >100 (bad)

Accuracy

Correcte voorspelscore:

1def accuracy(predictions, labels):
2    correct = sum(p == l for p, l in zip(predictions, labels))
3    return correct / len(labels)

F1‑score

Balans tussen Precision en Recall:

1from sklearn.metrics import f1_score, precision_score, recall_score
2
3def calculate_metrics(predictions, labels):
4    return {
5        "precision": precision_score(labels, predictions, average="weighted"),
6        "recall": recall_score(labels, predictions, average="weighted"),
7        "f1": f1_score(labels, predictions, average="weighted")
8    }
9## LLM‑benchmarks
10
11### MMLU (Massive Multitask Language Understanding)
12
13Meerkeuzevragen in 57 vakgebieden:
14
15```python
16def evaluate_mmlu(model, dataset):
17    results = {}
18    
19    for subject in dataset.subjects:
20        correct = 0
21        total = 0
22        
23        for question in dataset.get_questions(subject):
24            prompt = format_mcq_prompt(question)
25            response = model.generate(prompt)
26            predicted = extract_answer(response)
27            
28            if predicted == question.correct_answer:
29                correct += 1
30            total += 1
31        
32        results[subject] = correct / total
33    
34    return {
35        "subjects": results,
36        "average": sum(results.values()) / len(results)
37    }

MMLU‑resultaten (2024):

HellaSwag

Gezond verstand‑redeneren:

1Context: "A woman is outside with a bucket and a dog. 
2The dog is running around trying to avoid a bath. She..."
3
4Options:
5A) rinses the dog off with a hose (correct)
6B) calls the dog and feeds it
7C) throws the bucket at the dog
8D) walks into the house

TruthfulQA

Meting van hallucinatie en waarheidsgetrouwheid:

1def evaluate_truthfulness(model, questions):
2    truthful_count = 0
3    informative_count = 0
4    
5    for q in questions:
6        response = model.generate(q.question)
7        
8        # Human evaluation or classifier
9        is_truthful = check_truthfulness(response, q.ground_truth)
10        is_informative = check_informativeness(response)
11        
12        if is_truthful:
13            truthful_count += 1
14        if is_informative:
15            informative_count += 1
16    
17    return {
18        "truthful": truthful_count / len(questions),
19        "informative": informative_count / len(questions)
20    }

HumanEval

Codegeneratie‑capaciteit:

1def evaluate_humaneval(model, problems):
2    pass_at_1 = 0
3    pass_at_10 = 0
4    
5    for problem in problems:
6        solutions = [model.generate_code(problem.prompt) for _ in range(10)]
7        
8        passed = [run_tests(sol, problem.tests) for sol in solutions]
9        
10        if passed[0]:
11            pass_at_1 += 1
12        if any(passed):
13            pass_at_10 += 1
14    
15    return {
16        "pass@1": pass_at_1 / len(problems),
17        "pass@10": pass_at_10 / len(problems)
18    }

MT‑Bench

Kwaliteit van meerledige dialogen:

1def mt_bench_evaluate(model, conversations):
2    scores = []
3    
4    for conv in conversations:
5        # Multi-turn dialog
6        responses = []
7        for turn in conv.turns:
8            response = model.generate(turn.prompt, history=responses)
9            responses.append(response)
10        
11        # GPT judge scoring (1-10)
12        score = gpt4_judge(conv.turns, responses)
13        scores.append(score)
14    
15    return sum(scores) / len(scores)
16## RAG-evaluatie
17
18### Retrieval-metrics
19
20```python
21def retrieval_metrics(retrieved_docs, relevant_docs, k=10):
22    retrieved_k = retrieved_docs[:k]
23    relevant_set = set(relevant_docs)
24    
25    # Recall@K
26    retrieved_relevant = len(set(retrieved_k) & relevant_set)
27    recall_k = retrieved_relevant / len(relevant_set)
28    
29    # Precision@K
30    precision_k = retrieved_relevant / k
31    
32    # MRR (Mean Reciprocal Rank)
33    mrr = 0
34    for i, doc in enumerate(retrieved_k):
35        if doc in relevant_set:
36            mrr = 1 / (i + 1)
37            break
38    
39    return {
40        "recall@k": recall_k,
41        "precision@k": precision_k,
42        "mrr": mrr
43    }

RAGAS-metrics

1from ragas import evaluate
2from ragas.metrics import faithfulness, answer_relevancy, context_precision
3
4def evaluate_rag(questions, answers, contexts, ground_truths):
5    dataset = {
6        "question": questions,
7        "answer": answers,
8        "contexts": contexts,
9        "ground_truth": ground_truths
10    }
11    
12    results = evaluate(
13        dataset,
14        metrics=[faithfulness, answer_relevancy, context_precision]
15    )
16    
17    return results

Tekstgeneratie-metrics

BLEU-score

1from nltk.translate.bleu_score import sentence_bleu
2
3def calculate_bleu(reference, candidate):
4    reference_tokens = [reference.split()]
5    candidate_tokens = candidate.split()
6    
7    return sentence_bleu(reference_tokens, candidate_tokens)

ROUGE-score

1from rouge_score import rouge_scorer
2
3def calculate_rouge(reference, candidate):
4    scorer = rouge_scorer.RougeScorer(['rouge1', 'rouge2', 'rougeL'])
5    scores = scorer.score(reference, candidate)
6    
7    return {
8        "rouge1": scores['rouge1'].fmeasure,
9        "rouge2": scores['rouge2'].fmeasure,
10        "rougeL": scores['rougeL'].fmeasure
11    }

BERTScore

Semantische gelijkenis:

1from bert_score import score
2
3def calculate_bertscore(references, candidates):
4    P, R, F1 = score(candidates, references, lang="tr")
5    return {
6        "precision": P.mean().item(),
7        "recall": R.mean().item(),
8        "f1": F1.mean().item()
9    }

LLM-as-Judge

Evaluatie met GPT:

1def llm_judge(response, criteria):
2    prompt = f"""Evaluate the following response.
3
4Response: {response}
5
6Evaluation criteria:
7{criteria}
8
9Rate from 1-10 and explain your reasoning.
10JSON format: {{"score": X, "reasoning": "..."}}
11"""
12    
13    result = client.chat.completions.create(
14        model="gpt-4-turbo",
15        response_format={"type": "json_object"},
16        messages=[{"role": "user", "content": prompt}]
17    )
18    
19    return json.loads(result.choices[0].message.content)

A/B-testframework

1class ModelABTest:
2    def __init__(self, model_a, model_b):
3        self.model_a = model_a
4        self.model_b = model_b
5        self.results = {"a_wins": 0, "b_wins": 0, "ties": 0}
6    
7    def compare(self, prompt):
8        response_a = self.model_a.generate(prompt)
9        response_b = self.model_b.generate(prompt)
10        
11        # Blind comparison with LLM judge
12        winner = self.judge_comparison(prompt, response_a, response_b)
13        
14        self.results[f"{winner}_wins"] += 1
15        
16        return {
17            "response_a": response_a,
18            "response_b": response_b,
19            "winner": winner
20        }
21    
22    def get_statistics(self):
23        total = sum(self.results.values())
24        return {
25            "model_a_win_rate": self.results["a_wins"] / total,
26            "model_b_win_rate": self.results["b_wins"] / total,
27            "tie_rate": self.results["ties"] / total
28        }
29## Leaderboard vergelijking
30
31### Open LLM Leaderboard
32

1
2## Enterprise-evaluatie
3
4### Aangepaste benchmark
5
6```python
7class EnterpriseEvaluation:
8    def __init__(self, model, test_cases):
9        self.model = model
10        self.test_cases = test_cases
11    
12    def evaluate(self):
13        results = {
14            "accuracy": [],
15            "latency": [],
16            "cost": [],
17            "safety": []
18        }
19        
20        for case in self.test_cases:
21            start = time.time()
22            response = self.model.generate(case.prompt)
23            latency = time.time() - start
24            
25            results["latency"].append(latency)
26            results["accuracy"].append(
27                self.check_accuracy(response, case.expected)
28            )
29            results["safety"].append(
30                self.check_safety(response)
31            )
32        
33        return {
34            "avg_accuracy": np.mean(results["accuracy"]),
35            "p95_latency": np.percentile(results["latency"], 95),
36            "safety_rate": np.mean(results["safety"])
37        }

Conclusie

Model-evaluatie is een cruciale stap voor het succes van AI‑projecten. Met de juiste statistieken en benchmarks kun je weloverwogen modelkeuzes maken en zorgen voor voortdurende verbetering.

Bij Veni AI bieden we enterprise‑modellenevaluatieservices aan.