Chapter 7: Capabilities and Limitations of LLMs

Performance(N) = {
  Random level,     if N < N_critical
  Rapid improvement, if N ≥ N_critical
}

# Zero-shot classification example
def zero_shot_classification(model, text, candidate_labels):
    """
    Zero-shot text classification using pretrained models
    """
    prompt_template = "Text: {text}\nThis text is about: {label}"
 
    scores = []
    for label in candidate_labels:
        # Construct prompt
        prompt = prompt_template.format(text=text, label=label)
 
        # Calculate conditional probability
        prob = model.compute_conditional_probability(prompt)
        scores.append(prob)
 
    # Select label with highest probability
    predicted_label = candidate_labels[np.argmax(scores)]
    return predicted_label
 
# Practical application example
text = "The new smartphone features advanced AI capabilities"
labels = ["technology", "sports", "cooking", "finance"]
prediction = zero_shot_classification(model, text, labels)
# Output: "technology"

# Zero-shot execution of complex instructions
def zero_shot_instruction_following(model, instruction, context=""):
    """
    Zero-shot instruction execution framework
    """
    # Instruction parsing
    task_type = identify_task_type(instruction)
    parameters = extract_parameters(instruction)
 
    # Construct prompt based on task type
    if task_type == "summarization":
        prompt = f"Summarize the following text:\n{context}"
    elif task_type == "translation":
        prompt = f"Translate to {parameters['target_lang']}:\n{context}"
    elif task_type == "analysis":
        prompt = f"Analyze the following from a {parameters['perspective']} perspective:\n{context}"
 
    # Generate response
    response = model.generate(prompt)
    return response
 
# Application example
instruction = "Please analyze this text from a sentiment perspective"
context = "I am extremely disappointed with the service quality."
result = zero_shot_instruction_following(model, instruction, context)

class ZeroShotEvaluator:
    def __init__(self, model, test_suite):
        self.model = model
        self.test_suite = test_suite
 
    def evaluate_classification(self, dataset):
        """Evaluate zero-shot classification capability"""
        correct = 0
        total = len(dataset)
 
        for sample in dataset:
            prediction = self.model.zero_shot_predict(
                sample['text'],
                sample['candidate_labels']
            )
            if prediction == sample['true_label']:
                correct += 1
 
        accuracy = correct / total
        return accuracy
 
    def evaluate_task_transfer(self, source_tasks, target_tasks):
        """Evaluate task transfer capability"""
        transfer_scores = {}
 
        for target_task in target_tasks:
            # Test on target task without training
            score = self.test_on_task(target_task)
            transfer_scores[target_task.name] = score
 
        return transfer_scores
 
    def analyze_failure_cases(self, predictions, ground_truth):
        """Analyze failure cases"""
        failures = []
        for pred, true in zip(predictions, ground_truth):
            if pred != true:
                failure_analysis = self.diagnose_failure(pred, true)
                failures.append(failure_analysis)
 
        return self.categorize_failures(failures)

def assess_knowledge_boundaries(model, domain_questions):
    """Assess model knowledge boundaries"""
    boundary_analysis = {
        'confident_correct': [],
        'confident_wrong': [],
        'uncertain_correct': [],
        'uncertain_wrong': []
    }
 
    for question in domain_questions:
        # Get answer and confidence
        answer, confidence = model.answer_with_confidence(question)
        correctness = verify_answer(answer, question.ground_truth)
 
        # Categorize boundary cases
        if confidence > 0.8:
            if correctness:
                boundary_analysis['confident_correct'].append(question)
            else:
                boundary_analysis['confident_wrong'].append(question)
        else:
            if correctness:
                boundary_analysis['uncertain_correct'].append(question)
            else:
                boundary_analysis['uncertain_wrong'].append(question)
 
    return boundary_analysis

class FewShotLearner:
    def __init__(self, model):
        self.model = model
 
    def construct_few_shot_prompt(self, examples, query, task_description=""):
        """Construct few-shot learning prompt"""
        prompt = task_description + "\n\n" if task_description else ""
 
        # Add examples
        for i, example in enumerate(examples, 1):
            prompt += f"Example {i}:\n"
            prompt += f"Input: {example['input']}\n"
            prompt += f"Output: {example['output']}\n\n"
 
        # Add query
        prompt += f"Now solve this:\nInput: {query}\nOutput:"
 
        return prompt
 
    def optimize_example_selection(self, candidate_examples, query, k=3):
        """Optimize example selection strategy"""
        # Calculate similarity between query and candidate examples
        similarities = []
        for example in candidate_examples:
            sim = self.compute_similarity(query, example['input'])
            similarities.append((sim, example))
 
        # Select k most similar examples
        similarities.sort(reverse=True)
        selected_examples = [ex for _, ex in similarities[:k]]
 
        return selected_examples
 
    def compute_similarity(self, text1, text2):
        """Calculate text similarity"""
        # Use semantic embeddings to compute similarity
        emb1 = self.model.get_embedding(text1)
        emb2 = self.model.get_embedding(text2)
 
        cosine_sim = np.dot(emb1, emb2) / (np.linalg.norm(emb1) * np.linalg.norm(emb2))
        return cosine_sim

def dynamic_example_selection(model, query, example_pool, selection_strategy="diversity"):
    """Dynamic example selection"""
 
    if selection_strategy == "similarity":
        # Similarity-based selection
        return select_by_similarity(query, example_pool)
 
    elif selection_strategy == "diversity":
        # Diversity-based selection
        return select_by_diversity(example_pool)
 
    elif selection_strategy == "difficulty":
        # Difficulty-progressive selection
        return select_by_difficulty(query, example_pool)
 
    elif selection_strategy == "adaptive":
        # Adaptive selection strategy
        return adaptive_selection(model, query, example_pool)
 
def adaptive_selection(model, query, example_pool, max_examples=5):
    """Adaptive example selection"""
    selected_examples = []
    performance_history = []
 
    # Gradually add examples, monitor performance changes
    for candidate in example_pool:
        # Temporarily add candidate example
        temp_examples = selected_examples + [candidate]
 
        # Test performance on validation set
        performance = evaluate_with_examples(model, temp_examples, validation_set)
 
        # Keep example if performance improves
        if not performance_history or performance > max(performance_history):
            selected_examples.append(candidate)
            performance_history.append(performance)
 
            if len(selected_examples) >= max_examples:
                break
 
    return selected_examples

def analyze_few_shot_learning_curve(model, task, max_shots=10):
    """Analyze few-shot learning curve"""
    learning_curve = []
 
    for n_shots in range(1, max_shots + 1):
        # Multiple sampling of different example combinations
        scores = []
        for trial in range(10):  # Average over 10 trials
            examples = random.sample(task.train_examples, n_shots)
            score = evaluate_few_shot(model, examples, task.test_set)
            scores.append(score)
 
        avg_score = np.mean(scores)
        std_score = np.std(scores)
 
        learning_curve.append({
            'n_shots': n_shots,
            'mean_score': avg_score,
            'std_score': std_score
        })
 
    return learning_curve
 
def identify_learning_phases(learning_curve):
    """Identify learning phases"""
    phases = {
        'rapid_learning': [],  # Rapid learning phase
        'plateau': [],         # Plateau phase
        'overfitting': []      # Overfitting phase
    }
 
    for i in range(1, len(learning_curve)):
        current = learning_curve[i]['mean_score']
        previous = learning_curve[i-1]['mean_score']
 
        improvement = current - previous
 
        if improvement > 0.05:  # Significant improvement
            phases['rapid_learning'].append(i)
        elif abs(improvement) < 0.01:  # Almost no change
            phases['plateau'].append(i)
        elif improvement < -0.02:  # Performance degradation
            phases['overfitting'].append(i)
 
    return phases

class ContextualLearningAnalyzer:
    def __init__(self, model):
        self.model = model
 
    def analyze_pattern_extraction(self, examples):
        """Analyze pattern extraction capability"""
        patterns = {}
 
        # Identify input-output patterns
        for example in examples:
            input_features = self.extract_features(example['input'])
            output_features = self.extract_features(example['output'])
 
            # Establish feature mapping relationships
            for in_feat, out_feat in zip(input_features, output_features):
                pattern_key = (in_feat.type, out_feat.type)
                if pattern_key not in patterns:
                    patterns[pattern_key] = []
                patterns[pattern_key].append((in_feat.value, out_feat.value))
 
        # Analyze pattern consistency
        pattern_consistency = {}
        for pattern_key, mappings in patterns.items():
            consistency_score = self.compute_consistency(mappings)
            pattern_consistency[pattern_key] = consistency_score
 
        return pattern_consistency
 
    def test_generalization(self, learned_patterns, test_cases):
        """Test generalization capability"""
        generalization_results = []
 
        for test_case in test_cases:
            # Apply learned patterns
            predicted_output = self.apply_patterns(learned_patterns, test_case['input'])
 
            # Evaluate generalization quality
            generalization_score = self.evaluate_generalization(
                predicted_output,
                test_case['expected_output']
            )
 
            generalization_results.append({
                'test_case': test_case,
                'prediction': predicted_output,
                'score': generalization_score
            })
 
        return generalization_results

def study_context_length_effects(model, task, context_lengths=[1, 3, 5, 10, 20]):
    """Study impact of context length on learning effects"""
    results = {}
 
    for context_len in context_lengths:
        # Control context length
        context_scores = []
 
        for trial in range(20):  # Multiple trials
            # Randomly select context of specified length
            context_examples = random.sample(task.examples, context_len)
 
            # Test learning effects
            score = evaluate_contextual_learning(model, context_examples, task.test_queries)
            context_scores.append(score)
 
        results[context_len] = {
            'mean_score': np.mean(context_scores),
            'std_score': np.std(context_scores),
            'scores': context_scores
        }
 
    # Analyze trends
    optimal_length = find_optimal_context_length(results)
    diminishing_returns_point = find_diminishing_returns(results)
 
    return {
        'results': results,
        'optimal_length': optimal_length,
        'diminishing_returns_point': diminishing_returns_point
    }
 
def analyze_context_utilization(model, context_examples, query):
    """Analyze context utilization"""
    # Calculate importance of each example
    example_importance = []
 
    for i, example in enumerate(context_examples):
        # Performance after removing this example
        reduced_context = context_examples[:i] + context_examples[i+1:]
        reduced_performance = evaluate_with_context(model, reduced_context, query)
 
        # Performance with full context
        full_performance = evaluate_with_context(model, context_examples, query)
 
        # Importance of this example = performance loss after removal
        importance = full_performance - reduced_performance
        example_importance.append(importance)
 
    return example_importance

class MathematicalReasoningEvaluator:
    def __init__(self, model):
        self.model = model
 
    def evaluate_arithmetic_reasoning(self, problems):
        """Evaluate arithmetic reasoning capability"""
        results = {
            'basic_arithmetic': [],
            'word_problems': [],
            'multi_step_problems': [],
            'algebraic_reasoning': []
        }
 
        for problem in problems:
            # Classify problem type
            problem_type = self.classify_problem_type(problem)
 
            # Get model solution
            solution = self.model.solve_math_problem(problem.question)
 
            # Evaluate solution quality
            evaluation = self.evaluate_solution(solution, problem.correct_answer)
 
            results[problem_type].append(evaluation)
 
        return results
 
    def analyze_reasoning_steps(self, problem, solution):
        """Analyze reasoning steps"""
        steps = self.extract_reasoning_steps(solution)
 
        step_analysis = []
        for i, step in enumerate(steps):
            analysis = {
                'step_number': i + 1,
                'operation': self.identify_operation(step),
                'correctness': self.verify_step(step, problem.context),
                'logical_consistency': self.check_consistency(step, steps[:i])
            }
            step_analysis.append(analysis)
 
        return step_analysis
 
    def identify_error_patterns(self, failed_solutions):
        """Identify error patterns"""
        error_patterns = {
            'computational_errors': [],
            'logical_errors': [],
            'conceptual_errors': [],
            'procedural_errors': []
        }
 
        for solution in failed_solutions:
            errors = self.extract_errors(solution)
            for error in errors:
                error_type = self.classify_error(error)
                error_patterns[error_type].append(error)
 
        return error_patterns

def evaluate_logical_reasoning(model, reasoning_tasks):
    """Evaluate logical reasoning capability"""
    reasoning_types = {
        'deductive': [],  # Deductive reasoning
        'inductive': [],  # Inductive reasoning
        'abductive': [],  # Abductive reasoning
        'analogical': []  # Analogical reasoning
    }
 
    for task in reasoning_tasks:
        # Get model reasoning process
        reasoning_trace = model.generate_reasoning_trace(task.premise)
 
        # Evaluate reasoning quality
        evaluation = {
            'premise_understanding': evaluate_premise_understanding(reasoning_trace, task.premise),
            'logical_validity': check_logical_validity(reasoning_trace),
            'conclusion_correctness': verify_conclusion(reasoning_trace.conclusion, task.expected_conclusion),
            'reasoning_coherence': assess_coherence(reasoning_trace.steps)
        }
 
        reasoning_types[task.type].append(evaluation)
 
    return reasoning_types
 
def analyze_reasoning_failure_modes(model, failed_cases):
    """Analyze reasoning failure modes"""
    failure_modes = {
        'premise_misunderstanding': 0,
        'logical_fallacies': 0,
        'incomplete_reasoning': 0,
        'knowledge_gaps': 0,
        'attention_errors': 0
    }
 
    for case in failed_cases:
        # Analyze failure causes
        failure_analysis = diagnose_reasoning_failure(case)
 
        for failure_type in failure_analysis:
            failure_modes[failure_type] += 1
 
    return failure_modes

class CreativeReasoningAssessment:
    def __init__(self, model):
        self.model = model
 
    def evaluate_creative_problem_solving(self, open_ended_problems):
        """Evaluate creative problem solving"""
        creativity_metrics = []
 
        for problem in open_ended_problems:
            solutions = self.model.generate_multiple_solutions(problem, num_solutions=5)
 
            # Evaluate creativity metrics
            creativity_score = {
                'fluency': len(solutions),  # Fluency: number of solutions
                'flexibility': self.measure_flexibility(solutions),  # Flexibility: method diversity
                'originality': self.measure_originality(solutions),  # Originality: novelty degree
                'elaboration': self.measure_elaboration(solutions)   # Elaboration: detail richness
            }
 
            creativity_metrics.append(creativity_score)
 
        return creativity_metrics
 
    def measure_flexibility(self, solutions):
        """Measure cognitive flexibility"""
        # Analyze different approaches or perspectives used in solutions
        approaches = []
        for solution in solutions:
            approach = self.categorize_approach(solution)
            approaches.append(approach)
 
        # Calculate method diversity
        unique_approaches = len(set(approaches))
        flexibility_score = unique_approaches / len(solutions)
 
        return flexibility_score
 
    def measure_originality(self, solutions):
        """Measure originality"""
        originality_scores = []
 
        for solution in solutions:
            # Compare with common solutions
            similarity_to_common = self.compare_to_common_solutions(solution)
            originality = 1 - similarity_to_common
            originality_scores.append(originality)
 
        return np.mean(originality_scores)

class HallucinationAnalyzer:
    def __init__(self):
        self.hallucination_types = {
            'factual_hallucination': {
                'description': 'Generating information that contradicts facts',
                'examples': ['incorrect historical dates', 'false statistical data', 'non-existent character relationships'],
                'severity': 'high'
            },
            'logical_hallucination': {
                'description': 'Generating logically inconsistent content',
                'examples': ['self-contradictory statements', 'reasoning violating causality'],
                'severity': 'high'
            },
            'contextual_hallucination': {
                'description': 'Generating content inconsistent with context',
                'examples': ['off-topic responses', 'ignoring important constraints'],
                'severity': 'medium'
            },
            'stylistic_hallucination': {
                'description': 'Generating content that violates required style',
                'examples': ['inappropriate tone', 'format errors'],
                'severity': 'low'
            }
        }
 
    def detect_hallucination(self, generated_text, context, knowledge_base):
        """Detect hallucinatory content"""
        detection_results = {}
 
        # Factual verification
        factual_claims = self.extract_factual_claims(generated_text)
        factual_hallucinations = []
 
        for claim in factual_claims:
            if not self.verify_against_knowledge_base(claim, knowledge_base):
                factual_hallucinations.append(claim)
 
        detection_results['factual'] = factual_hallucinations
 
        # Logical consistency verification
        logical_consistency = self.check_logical_consistency(generated_text)
        detection_results['logical'] = logical_consistency
 
        # Contextual consistency verification
        contextual_consistency = self.check_contextual_consistency(generated_text, context)
        detection_results['contextual'] = contextual_consistency
 
        return detection_results
 
    def analyze_hallucination_patterns(self, model, test_dataset):
        """Analyze hallucination patterns"""
        hallucination_patterns = {
            'frequency_by_task': {},
            'common_error_types': {},
            'triggering_contexts': [],
            'severity_distribution': {}
        }
 
        for sample in test_dataset:
            generated = model.generate(sample.prompt)
            hallucinations = self.detect_hallucination(
                generated,
                sample.context,
                sample.knowledge_base
            )
 
            # Statistical analysis
            task_type = sample.task_type
            if task_type not in hallucination_patterns['frequency_by_task']:
                hallucination_patterns['frequency_by_task'][task_type] = 0
 
            if any(hallucinations.values()):
                hallucination_patterns['frequency_by_task'][task_type] += 1
 
                # Analyze triggering contexts
                if self.is_high_risk_context(sample.context):
                    hallucination_patterns['triggering_contexts'].append(sample.context)
 
        return hallucination_patterns

class HallucinationMitigation:
    def __init__(self, model, knowledge_base):
        self.model = model
        self.knowledge_base = knowledge_base
 
    def implement_retrieval_augmentation(self, query):
        """Implement retrieval augmentation to mitigate hallucinations"""
        # Retrieve relevant reliable information
        relevant_docs = self.knowledge_base.retrieve(query, top_k=5)
 
        # Build augmented prompt
        augmented_prompt = self.build_rag_prompt(query, relevant_docs)
 
        # Generate response
        response = self.model.generate(augmented_prompt)
 
        # Post-processing verification
        verified_response = self.post_verification(response, relevant_docs)
 
        return verified_response
 
    def apply_uncertainty_quantification(self, query, num_samples=5):
        """Apply uncertainty quantification"""
        responses = []
 
        # Multiple sampling generations
        for _ in range(num_samples):
            response = self.model.generate(query, temperature=0.7)
            responses.append(response)
 
        # Analyze consistency
        consistency_score = self.compute_response_consistency(responses)
 
        # If consistency is low, mark as uncertain
        if consistency_score < 0.6:
            return {
                'response': self.generate_hedged_response(responses[0]),
                'confidence': 'low',
                'uncertainty_flag': True
            }
        else:
            return {
                'response': self.consolidate_responses(responses),
                'confidence': 'high',
                'uncertainty_flag': False
            }
 
    def implement_fact_checking_pipeline(self, generated_text):
        """Implement fact-checking pipeline"""
        # Extract verifiable claims
        claims = self.extract_verifiable_claims(generated_text)
 
        verification_results = []
        for claim in claims:
            # Multi-source verification
            verification = {
                'claim': claim,
                'kb_verification': self.verify_against_kb(claim),
                'external_verification': self.verify_with_external_apis(claim),
                'confidence_score': self.compute_verification_confidence(claim)
            }
            verification_results.append(verification)
 
        # Rewrite or annotate unreliable content
        corrected_text = self.apply_corrections(generated_text, verification_results)
 
        return corrected_text, verification_results

class BiasAnalyzer:
    def __init__(self):
        self.bias_categories = {
            'demographic_bias': ['gender', 'race', 'age', 'religion', 'nationality'],
            'social_bias': ['socioeconomic_status', 'education', 'occupation'],
            'cognitive_bias': ['confirmation_bias', 'availability_heuristic', 'anchoring_bias'],
            'cultural_bias': ['western_centrism', 'language_preference', 'cultural_assumptions']
        }
 
    def analyze_training_data_bias(self, dataset):
        """Analyze training data bias"""
        bias_analysis = {}
 
        for category, attributes in self.bias_categories.items():
            category_analysis = {}
 
            for attribute in attributes:
                # Statistical attribute distribution
                distribution = self.compute_attribute_distribution(dataset, attribute)
 
                # Calculate skewness
                skewness = self.compute_skewness(distribution)
 
                # Analyze representation gaps
                representation_gaps = self.identify_representation_gaps(distribution)
 
                category_analysis[attribute] = {
                    'distribution': distribution,
                    'skewness': skewness,
                    'representation_gaps': representation_gaps
                }
 
            bias_analysis[category] = category_analysis
 
        return bias_analysis
 
    def evaluate_model_bias(self, model, bias_test_suite):
        """Evaluate model bias"""
        bias_evaluation = {}
 
        for test_category, test_cases in bias_test_suite.items():
            category_results = []
 
            for test_case in test_cases:
                # Generate model response
                response = model.generate(test_case.prompt)
 
                # Analyze bias metrics
                bias_score = self.compute_bias_score(response, test_case.expected_neutral)
 
                # Classify bias type
                bias_type = self.classify_bias_type(response, test_case.target_attribute)
 
                category_results.append({
                    'test_case': test_case,
                    'response': response,
                    'bias_score': bias_score,
                    'bias_type': bias_type
                })
 
            bias_evaluation[test_category] = category_results
 
        return bias_evaluation

def measure_fairness_metrics(model, evaluation_dataset):
    """Measure fairness metrics"""
    fairness_metrics = {}
 
    # Performance differences across groups
    group_performance = {}
    for group in evaluation_dataset.demographic_groups:
        group_data = evaluation_dataset.filter_by_group(group)
        performance = evaluate_model_performance(model, group_data)
        group_performance[group] = performance
 
    # Calculate fairness metrics
    fairness_metrics['demographic_parity'] = compute_demographic_parity(group_performance)
    fairness_metrics['equalized_odds'] = compute_equalized_odds(group_performance)
    fairness_metrics['calibration'] = compute_calibration_fairness(group_performance)
 
    # Analyze bias amplification
    bias_amplification = analyze_bias_amplification(model, evaluation_dataset)
    fairness_metrics['bias_amplification'] = bias_amplification
 
    return fairness_metrics
 
def implement_bias_mitigation_strategies(model, training_data):
    """Implement bias mitigation strategies"""
    mitigation_strategies = {
        'data_augmentation': augment_underrepresented_groups(training_data),
        'adversarial_debiasing': apply_adversarial_training(model),
        'prompt_engineering': design_unbiased_prompts(),
        'post_processing': implement_bias_correction_filters()
    }
 
    return mitigation_strategies

class ResourceConsumptionAnalyzer:
    def __init__(self):
        self.metrics = ['gpu_hours', 'energy_consumption', 'carbon_footprint', 'monetary_cost']
 
    def analyze_training_costs(self, model_config, training_config):
        """Analyze training costs"""
        # Calculate parameter scale
        parameter_count = self.compute_parameter_count(model_config)
 
        # Estimate training time
        training_time = self.estimate_training_time(
            parameter_count,
            training_config.dataset_size,
            training_config.hardware_config
        )
 
        # Energy consumption analysis
        energy_consumption = self.compute_energy_consumption(
            training_time,
            training_config.hardware_config
        )
 
        # Carbon footprint calculation
        carbon_footprint = self.compute_carbon_footprint(
            energy_consumption,
            training_config.energy_source
        )
 
        # Economic cost
        monetary_cost = self.compute_monetary_cost(
            training_time,
            training_config.cloud_pricing
        )
 
        return {
            'parameter_count': parameter_count,
            'training_time_hours': training_time,
            'energy_kwh': energy_consumption,
            'carbon_kg_co2': carbon_footprint,
            'cost_usd': monetary_cost
        }
 
    def analyze_inference_costs(self, model, workload_profile):
        """Analyze inference costs"""
        inference_metrics = {}
 
        for scenario in workload_profile.scenarios:
            # Calculate latency
            latency = self.measure_inference_latency(model, scenario.inputs)
 
            # Calculate throughput
            throughput = self.measure_throughput(model, scenario.batch_size)
 
            # Energy consumption analysis
            energy_per_token = self.compute_energy_per_token(model, scenario.hardware)
 
            # Cost-effectiveness analysis
            cost_efficiency = self.compute_cost_efficiency(latency, throughput, energy_per_token)
 
            inference_metrics[scenario.name] = {
                'latency_ms': latency,
                'throughput_tokens_per_sec': throughput,
                'energy_per_token_mj': energy_per_token,
                'cost_efficiency': cost_efficiency
            }
 
        return inference_metrics

def implement_efficiency_optimizations(model):
    """Implement efficiency optimization strategies"""
    optimization_techniques = {
        'model_compression': {
            'quantization': apply_quantization(model),
            'pruning': apply_network_pruning(model),
            'knowledge_distillation': train_smaller_student_model(model)
        },
        'inference_optimization': {
            'kv_cache': implement_key_value_caching(),
            'speculative_decoding': enable_speculative_decoding(),
            'batch_optimization': optimize_batch_processing()
        },
        'hardware_optimization': {
            'mixed_precision': enable_mixed_precision_training(),
            'gradient_checkpointing': apply_gradient_checkpointing(),
            'model_parallelism': implement_model_parallelism()
        }
    }
 
    return optimization_techniques
 
def design_green_ai_practices():
    """Design green AI practices"""
    green_practices = {
        'efficient_architectures': {
            'description': 'Design more computationally efficient architectures',
            'techniques': ['MobileNets', 'EfficientNets', 'Sparse Transformers']
        },
        'renewable_energy': {
            'description': 'Use renewable energy for training',
            'strategies': ['choose green data centers', 'time scheduling optimization', 'carbon-aware scheduling']
        },
        'model_sharing': {
            'description': 'Share pretrained models to reduce duplicate training',
            'benefits': ['reduce overall computational demand', 'promote research collaboration', 'lower barriers']
        },
        'lifecycle_assessment': {
            'description': 'Assess full lifecycle environmental impact of models',
            'components': ['training phase', 'deployment phase', 'maintenance phase', 'disposal phase']
        }
    }
 
    return green_practices

class KnowledgeTimelinessAnalyzer:
    def __init__(self, model, knowledge_cutoff_date):
        self.model = model
        self.knowledge_cutoff = knowledge_cutoff_date
 
    def assess_knowledge_freshness(self, test_queries):
        """Assess knowledge freshness"""
        freshness_analysis = {
            'up_to_date': [],
            'outdated': [],
            'unknown_recent': []
        }
 
        for query in test_queries:
            response = self.model.generate(query.question)
 
            # Determine time sensitivity of query
            time_sensitivity = self.assess_time_sensitivity(query)
 
            if time_sensitivity == 'high':
                # Check for outdated information
                if self.contains_outdated_info(response, query.current_facts):
                    freshness_analysis['outdated'].append({
                        'query': query,
                        'response': response,
                        'outdated_elements': self.identify_outdated_elements(response)
                    })
                elif self.lacks_recent_info(response, query.recent_developments):
                    freshness_analysis['unknown_recent'].append({
                        'query': query,
                        'response': response,
                        'missing_info': query.recent_developments
                    })
                else:
                    freshness_analysis['up_to_date'].append(query)
 
        return freshness_analysis
 
    def identify_knowledge_gaps(self, domain_queries):
        """Identify knowledge gaps"""
        knowledge_gaps = {}
 
        for domain, queries in domain_queries.items():
            domain_gaps = []
 
            for query in queries:
                response = self.model.generate(query.question)
 
                # Analyze knowledge gap types
                gap_analysis = self.analyze_knowledge_gap(response, query)
 
                if gap_analysis['has_gap']:
                    domain_gaps.append({
                        'query': query,
                        'gap_type': gap_analysis['gap_type'],
                        'severity': gap_analysis['severity'],
                        'required_update': gap_analysis['required_update']
                    })
 
            knowledge_gaps[domain] = domain_gaps
 
        return knowledge_gaps

def design_knowledge_update_strategies():
    """Design knowledge update strategies"""
    update_strategies = {
        'continuous_learning': {
            'description': 'Continuous learning mechanisms',
            'approaches': [
                'incremental_training',
                'online_learning',
                'meta_learning'
            ],
            'challenges': [
                'catastrophic_forgetting',
                'stability_plasticity_dilemma',
                'computational_overhead'
            ]
        },
        'retrieval_augmentation': {
            'description': 'Retrieval-augmented updates',
            'components': [
                'real_time_knowledge_base',
                'dynamic_retrieval',
                'knowledge_fusion'
            ],
            'advantages': [
                'immediate_access_to_new_info',
                'no_model_retraining_required',
                'controllable_knowledge_sources'
            ]
        },
        'periodic_retraining': {
            'description': 'Periodic retraining',
            'scheduling': [
                'time_based_updates',
                'performance_triggered_updates',
                'domain_specific_updates'
            ],
            'considerations': [
                'update_frequency_optimization',
                'cost_benefit_analysis',
                'version_control_management'
            ]
        }
    }
 
    return update_strategies
 
class AdaptiveKnowledgeUpdater:
    def __init__(self, model):
        self.model = model
        self.update_history = []
 
    def monitor_knowledge_drift(self, validation_set):
        """Monitor knowledge drift"""
        current_performance = self.evaluate_model(validation_set)
 
        if self.update_history:
            previous_performance = self.update_history[-1]['performance']
            drift_score = self.compute_drift_score(current_performance, previous_performance)
 
            if drift_score > self.drift_threshold:
                return {
                    'drift_detected': True,
                    'drift_score': drift_score,
                    'recommended_action': 'knowledge_update_required'
                }
 
        return {'drift_detected': False}
 
    def prioritize_update_domains(self, knowledge_gaps):
        """Prioritize update domains"""
        priority_scores = {}
 
        for domain, gaps in knowledge_gaps.items():
            # Calculate priority scores
            urgency_score = self.compute_urgency_score(gaps)
            impact_score = self.compute_impact_score(domain)
            feasibility_score = self.compute_feasibility_score(domain)
 
            priority_score = (
                0.4 * urgency_score +
                0.4 * impact_score +
                0.2 * feasibility_score
            )
 
            priority_scores[domain] = priority_score
 
        # Sort by priority
        sorted_domains = sorted(priority_scores.items(), key=lambda x: x[1], reverse=True)
 
        return sorted_domains

class ExplainabilityAnalyzer:
    def __init__(self, model):
        self.model = model
 
    def assess_explainability_needs(self, application_domains):
        """Assess interpretability needs"""
        explainability_requirements = {}
 
        for domain in application_domains:
            requirements = {
                'transparency_level': self.determine_transparency_needs(domain),
                'explanation_granularity': self.determine_granularity_needs(domain),
                'stakeholder_requirements': self.identify_stakeholders(domain),
                'regulatory_constraints': self.identify_regulations(domain)
            }
 
            # High-risk domains require higher interpretability
            if domain.risk_level == 'high':
                requirements.update({
                    'audit_trail': True,
                    'decision_rationale': 'detailed',
                    'uncertainty_quantification': True,
                    'bias_detection': True
                })
 
            explainability_requirements[domain.name] = requirements
 
        return explainability_requirements
 
    def implement_explanation_methods(self, explanation_type):
        """Implement explanation methods"""
        explanation_methods = {
            'local_explanations': {
                'attention_visualization': self.visualize_attention_patterns,
                'gradient_based_attribution': self.compute_gradient_attribution,
                'perturbation_analysis': self.analyze_input_perturbations,
                'counterfactual_explanations': self.generate_counterfactuals
            },
            'global_explanations': {
                'feature_importance': self.analyze_global_feature_importance,
                'concept_activation': self.analyze_concept_activation,
                'model_behavior_analysis': self.analyze_model_behavior_patterns,
                'decision_tree_approximation': self.approximate_with_decision_tree
            },
            'example_based_explanations': {
                'nearest_neighbors': self.find_similar_examples,
                'prototypes': self.identify_prototypical_examples,
                'influential_instances': self.find_influential_training_instances
            }
        }
 
        return explanation_methods[explanation_type]

def implement_interpretability_techniques(model):
    """Implement interpretability techniques"""
 
    # Attention visualization
    def visualize_attention_patterns(input_text, layer_idx=None):
        """Visualize attention patterns"""
        attention_weights = model.get_attention_weights(input_text, layer_idx)
 
        visualization = {
            'token_attention_matrix': attention_weights,
            'head_attention_patterns': analyze_head_patterns(attention_weights),
            'layer_attention_trends': analyze_layer_trends(attention_weights)
        }
 
        return visualization
 
    # Gradient attribution analysis
    def gradient_attribution_analysis(input_text, target_output):
        """Gradient attribution analysis"""
        gradients = model.compute_gradients(input_text, target_output)
 
        attribution_scores = {
            'token_importance': compute_token_importance(gradients),
            'feature_attribution': compute_feature_attribution(gradients),
            'layer_contribution': analyze_layer_contributions(gradients)
        }
 
        return attribution_scores
 
    # Counterfactual explanations
    def generate_counterfactual_explanations(input_text, target_change):
        """Generate counterfactual explanations"""
        counterfactuals = []
 
        # Systematically modify input
        for modification in generate_systematic_modifications(input_text):
            modified_input = apply_modification(input_text, modification)
            modified_output = model.generate(modified_input)
 
            if satisfies_counterfactual_criteria(modified_output, target_change):
                counterfactuals.append({
                    'original_input': input_text,
                    'modified_input': modified_input,
                    'modification': modification,
                    'output_change': modified_output
                })
 
        return counterfactuals
 
    # Concept activation analysis
    def concept_activation_analysis(concepts, test_inputs):
        """Concept activation analysis"""
        concept_scores = {}
 
        for concept in concepts:
            # Train concept classifier
            concept_classifier = train_concept_classifier(concept, model)
 
            # Test concept activation
            activations = []
            for input_text in test_inputs:
                activation = concept_classifier.predict(model.get_hidden_states(input_text))
                activations.append(activation)
 
            concept_scores[concept.name] = {
                'mean_activation': np.mean(activations),
                'activation_distribution': activations,
                'high_activation_examples': find_high_activation_examples(test_inputs, activations)
            }
 
        return concept_scores
 
def design_explainable_ai_framework():
    """Design explainable AI framework"""
    framework_components = {
        'explanation_interface': {
            'user_query_processing': 'process_explanation_requests',
            'explanation_generation': 'generate_appropriate_explanations',
            'visualization_rendering': 'render_visual_explanations',
            'interactive_exploration': 'enable_interactive_analysis'
        },
        'explanation_evaluation': {
            'faithfulness_metrics': 'measure_explanation_accuracy',
            'comprehensibility_assessment': 'evaluate_user_understanding',
            'completeness_analysis': 'assess_explanation_coverage',
            'stability_testing': 'test_explanation_consistency'
        },
        'stakeholder_adaptation': {
            'expert_explanations': 'technical_detailed_explanations',
            'layperson_explanations': 'simplified_accessible_explanations',
            'regulatory_compliance': 'audit_trail_documentation',
            'decision_support': 'actionable_insights_provision'
        }
    }
 
    return framework_components