Fine-Tuning LLMs for Text Generation

5.

Evaluation and Quality Assessment

5.1.

Quantitative Evaluation Methods

5.1.1.

Perplexity Measurement

5.1.1.1.

Calculation Methodology

5.1.1.2.

Interpretation Guidelines

5.1.1.3.

Limitations and Context

5.1.2.

Task-Specific Metrics

5.1.2.1.

5.1.2.1.1.

N-gram Precision

5.1.2.1.2.

Brevity Penalty

5.1.2.1.3.

Translation Quality

5.1.2.2.

5.1.2.2.1.

5.1.2.2.2.

5.1.2.2.3.

5.1.2.2.4.

Summarization Evaluation

5.1.2.3.

Accuracy Measures

5.1.2.3.1.

Exact Match Accuracy

5.1.2.3.2.

Token-Level Accuracy

5.1.2.3.3.

Sequence-Level Accuracy

5.1.2.4.

Semantic Similarity Metrics

5.1.2.4.1.

Embedding-Based Similarity

5.1.2.4.2.

5.1.2.4.3.

Semantic Textual Similarity

5.1.3.

Automated Evaluation Frameworks

5.1.3.1.

Benchmark Datasets

5.1.3.2.

Evaluation Pipelines

5.1.3.3.

Comparative Analysis

5.2.

Qualitative Assessment Approaches

5.2.1.

Human Evaluation Methods

5.2.1.1.

Coherence Assessment

5.2.1.1.1.

5.2.1.1.2.

Consistency Checking

5.2.1.1.3.

Narrative Structure

5.2.1.2.

Relevance Evaluation

5.2.1.2.1.

Topic Adherence

5.2.1.2.2.

Context Appropriateness

5.2.1.2.3.

Information Accuracy

5.2.1.3.

Fluency Analysis

5.2.1.3.1.

Grammatical Correctness

5.2.1.3.2.

Natural Language Flow

5.2.1.3.3.

Readability Assessment

5.2.1.4.

Style and Tone Evaluation

5.2.1.4.1.

Voice Consistency

5.2.1.4.2.

Register Appropriateness

5.2.1.4.3.

Brand Alignment

5.2.2.

Comparative Evaluation

5.2.2.1.

A/B Testing Design

5.2.2.1.1.

Experimental Setup

5.2.2.1.2.

Statistical Significance

5.2.2.1.3.

Bias Mitigation

5.2.2.2.

Side-by-Side Comparisons

5.2.2.2.1.

Preference Ranking

5.2.2.2.2.

Quality Scoring

5.2.2.2.3.

Feature Analysis

5.2.3.

5.2.3.1.

Hallucination Detection

5.2.3.1.1.

Factual Accuracy Checking

5.2.3.1.2.

Source Verification

5.2.3.1.3.

Confidence Assessment

5.2.3.2.

Bias Identification

5.2.3.2.1.

Demographic Bias

5.2.3.2.2.

5.2.3.2.3.

5.3.

Debugging and Optimization

5.3.1.

Training Diagnostics

5.3.1.1.

Overfitting Identification

5.3.1.1.1.

Validation Loss Monitoring

5.3.1.1.2.

Generalization Gap Analysis

5.3.1.1.3.

Regularization Strategies

5.3.1.2.

Underfitting Recognition

5.3.1.2.1.

Learning Curve Analysis

5.3.1.2.2.

Capacity Assessment

5.3.1.2.3.

Data Sufficiency Evaluation

5.3.1.3.

Loss Curve Interpretation

5.3.1.3.1.

Training Dynamics

5.3.1.3.2.

Convergence Patterns

5.3.1.3.3.

Anomaly Detection

5.3.2.

Performance Optimization

5.3.2.1.

Hyperparameter Tuning

5.3.2.1.1.

5.3.2.1.2.

5.3.2.1.3.

Bayesian Optimization

5.3.2.2.

Architecture Modifications

5.3.2.2.1.

Layer Adjustments

5.3.2.2.2.

Attention Modifications

5.3.2.2.3.

Efficiency Improvements

5.3.3.

Iterative Improvement Process

5.3.3.1.

Data Augmentation

5.3.3.1.1.

Synthetic Data Generation

5.3.3.1.2.

Data Diversification

5.3.3.1.3.

Quality Enhancement

5.3.3.2.

Method Comparison

5.3.3.2.1.

PEFT Technique Evaluation

5.3.3.2.2.

Hybrid Approaches

5.3.3.2.3.

Performance Trade-offs

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