LLM Evaluation Framework

Reading Comprehension: Assess the model's ability to understand context, extract relevant information, and answer questions based on provided text. Key benchmarks include SQuAD, RACE, and CoQA, which evaluate different aspects of reading comprehension across varying text types and question complexities.

Semantic Parsing: Evaluate the model's capability to convert natural language into structured representations like logical forms, SQL queries, or API calls. This tests deeper understanding beyond surface-level comprehension.

Natural Language Inference: Measure the model's ability to determine logical relationships (entailment, contradiction, neutrality) between text passages. The GLUE and SuperGLUE benchmarks provide standardized tests for these capabilities.

Discourse Analysis: Assess understanding of conversational flow, rhetorical structure, and cross-sentence relationships. This includes coreference resolution, discourse coherence, and pragmatic inference.

Multilingual Comprehension: Evaluate comprehension capabilities across multiple languages using benchmarks like XNLI, MLQA, and XQuAD to determine linguistic generalization beyond English.

Logical Reasoning: Evaluate the model's ability to follow logical chains and draw valid conclusions using benchmarks like LogiQA, ReClor, and PrOntoQA. This includes deductive, inductive, and abductive reasoning patterns.

Mathematical Reasoning: Assess mathematical capabilities from basic arithmetic to complex problem-solving using benchmarks like GSM8K, MATH, and MathQA. Look beyond answer accuracy to evaluate solution processes and explanations.

Common Sense Reasoning: Measure the model's ability to apply everyday knowledge to situations using benchmarks like CommomsenseQA, PIQA, and HellaSwag. This evaluates implicit understanding that humans take for granted.

Counterfactual Reasoning: Test the model's ability to reason about hypothetical situations that differ from reality, requiring causal understanding and mental simulation.

Analogical Reasoning: Assess the ability to identify patterns and apply them to new situations, an important component of human-like intelligence and generalization.

Factual Knowledge: Evaluate the breadth and accuracy of the model's knowledge using benchmarks like TruthfulQA, MMLU, and NaturalQuestions. Stratify assessment across diverse domains (science, history, current events, etc.).

Knowledge Updating: Assess how effectively models can incorporate new information provided in the context to override or complement their parametric knowledge. This tests adaptability to new or changing information.

Citation Accuracy: Measure the model's ability to correctly attribute information to sources when requested, an important aspect of responsible knowledge generation.

Temporal Knowledge: Evaluate understanding of time-dependent facts and the model's awareness of its knowledge cutoff, including ability to acknowledge uncertainty for events beyond its training data.

Specialized Domain Knowledge: Test depth of knowledge in specialized fields like medicine, law, finance, or technical domains using domain-specific benchmarks and expert evaluation.

Coherence and Fluency: Assess the linguistic quality of generated text, including grammatical correctness, natural flow, and adherence to language conventions. Use both automated metrics (perplexity, BLEU) and human evaluation.

Stylistic Control: Evaluate the model's ability to adapt generation style to match specified tones, formality levels, or particular authors/genres. This tests flexibility in output presentation.

Abstractive Summarization: Measure the model's capability to condense information while preserving key points and generating novel phrasing rather than extractive copying. Benchmarks include CNN/DailyMail, XSum, and SummEval.

Content Planning and Structure: Assess how well models can plan and structure longer outputs, maintaining coherent narrative or logical progression across paragraphs and sections.

Creativity and Novelty: Evaluate the balance between originality and quality in creative writing tasks, including storytelling, poetry, and content creation scenarios.

Basic Instruction Adherence: Measure the model's ability to follow straightforward instructions, including format specifications, length requirements, and specific content inclusion/exclusion.

Complex Instruction Handling: Evaluate performance on multi-step instructions that require planning, sequential execution, and tracking progress across subtasks.

Constraint Satisfaction: Assess the model's ability to maintain multiple constraints simultaneously, such as generating content with specific inclusions, exclusions, and stylistic requirements.

Instruction Clarification: Test how models handle ambiguous or underspecified instructions, including their ability to ask clarifying questions or make reasonable assumptions when necessary.

Instruction Evolution: Evaluate how well models adapt to changing instructions within a conversation, including refinements, corrections, and completely new directions.

Tool Selection Accuracy: Assess the model's ability to correctly identify when to use available tools and which specific tools to call for a given task.

Parameter Construction: Evaluate how accurately the model constructs function call parameters, including proper formatting, data types, and adherence to schema definitions.

Multi-Tool Orchestration: Measure the capability to sequence multiple tool calls in the correct order to accomplish complex tasks, including handling dependencies between calls.

Result Interpretation: Test the model's ability to correctly interpret and incorporate tool outputs into its reasoning and response generation.

Recovery From Tool Errors: Evaluate how effectively the model handles tool execution failures, including error interpretation and appropriate fallback strategies or retries.

Query Formulation: Assess the model's ability to generate effective search queries from user questions or instructions, including identification of key terms and concepts.

Relevance Assessment: Evaluate how well the model distinguishes between relevant and irrelevant retrieved information, prioritizing the most useful content for the task at hand.

Information Synthesis: Measure the capability to integrate information from multiple retrieved sources, resolving conflicts and forming coherent understanding.

Attribution Quality: Test the model's ability to properly attribute information to retrieved sources, including direct quotations and paraphrased content.

Knowledge Gap Awareness: Assess how effectively the model identifies when retrieved information is insufficient and additional retrieval is needed.

Image Understanding: Evaluate the model's ability to comprehend and describe image content, including object recognition, scene understanding, and visual reasoning tasks.

Cross-Modal Reasoning: Assess how effectively the model can reason across text and visual information, drawing connections and inferences that require integrating both modalities.

Visual-Based Instruction Following: Test the model's capability to follow instructions that reference visual elements, such as "describe the object in the top left" or "explain the graph trend."

Technical Image Analysis: Evaluate performance on specialized visual content like charts, diagrams, code snippets, and technical drawings, which require domain-specific visual literacy.

Visual Content Generation Guidance: For models that pair with image generation systems, assess the quality of prompts and iterative guidance the LLM provides for visual creation tasks.

Latency Benchmarking: Measure response generation time across different types of queries and input lengths, both for first token generation and complete response delivery.

Token Efficiency: Evaluate the model's ability to accomplish tasks with minimal token usage, both in terms of prompt engineering and response verbosity.

Context Window Utilization: Assess how effectively the model handles long contexts, including information retrieval from early parts of the context and consistent reasoning across the full available window.

Resource Requirements: Benchmark computational resources needed for deployment, including GPU memory, throughput capabilities, and scaling characteristics with concurrent users.

Optimization Potential: Evaluate compatibility with performance optimization techniques like quantization, distillation, and caching, including the performance-quality tradeoffs of each approach.

Input Variation Handling: Test performance consistency across variations in input phrasing, formatting, and style to assess sensitivity to prompt wording.

Error Recovery: Evaluate the model's ability to recover from mistakes within a conversation, including self-correction and appropriate handling of user corrections.

Edge Case Performance: Assess behavior on unusual, ambiguous, or edge case inputs that test the boundaries of the model's capabilities and training distribution.

Consistency Over Time: Measure stability of responses when identical queries are presented at different times or in different conversation contexts.

Graceful Degradation: Test how performance degrades under suboptimal conditions like truncated inputs, ambiguous instructions, or knowledge gaps, looking for reasonable fallback behaviors rather than catastrophic failures.

API Compatibility: Assess the model's compatibility with standard API patterns, including streaming capabilities, metadata handling, and error reporting.

System Prompt Effectiveness: Evaluate how well the model responds to system prompts that establish persistent behavior patterns or role definitions throughout conversations.

Workflow Integration: Test the model's ability to maintain context and state when integrated into larger application workflows, including handling session persistence and context management.

Customization Options: Assess available methods for model customization, such as fine-tuning, prompt engineering, or retrieval augmentation, including their effectiveness and implementation complexity.

Versioning Impact: Evaluate stability across model version updates, including both minor and major version changes, to understand potential impacts on dependent applications.

Refusal Testing: Evaluate the model's ability to appropriately refuse harmful, illegal, or unethical requests across various categories and different levels of request obfuscation.

Content Moderation: Assess capabilities in identifying and flagging potentially harmful content in both inputs and outputs, including sensitivity across different severity levels.

Prompt Injection Resistance: Test resistance to adversarial prompts designed to override safety guidelines or system instructions, including both direct and indirect injection attempts.

Jailbreak Vulnerability: Evaluate susceptibility to various jailbreaking techniques that attempt to circumvent safety measures, including monitoring emerging evasion methods.

Personification Boundaries: Assess the model's adherence to appropriate boundaries when asked to impersonate individuals, entities, or roles that could enable harmful outputs.

Demographic Bias Assessment: Measure performance differences and content variations across demographically relevant dimensions, including race, gender, age, nationality, and other protected characteristics.

Stereotype Perpetuation: Evaluate tendency to generate or reinforce stereotypical representations of different groups in open-ended generation tasks.

Representation Balance: Assess default assumptions and examples provided by the model when demographic details are unspecified in the prompt.

Intersectional Analysis: Test for bias patterns that emerge at the intersection of multiple demographic dimensions, which may not be apparent in single-dimension analysis.

Bias Mitigation Effectiveness: Evaluate the impact of various bias mitigation techniques, including prompt engineering, dataset balancing, and model training approaches.

Hallucination Detection: Measure the model's tendency to generate factually incorrect information across different knowledge domains and confidence levels.

Uncertainty Expression: Evaluate how appropriately the model expresses uncertainty when information is incomplete, ambiguous, or beyond its knowledge scope.

Misinformation Resistance: Test the model's ability to identify and avoid amplifying common misinformation narratives or conspiracy theories.

Source Quality Discrimination: Assess capability to distinguish between reliable and unreliable information sources when presented with conflicting information.

Fact-Checking Capabilities: Evaluate the model's effectiveness in verifying claims against its parametric knowledge when explicitly asked to fact-check information.

PII Handling: Assess the model's ability to recognize and appropriately handle personally identifiable information in both inputs and outputs, including redaction when appropriate.

Training Data Leakage: Test for verbatim reproduction of sensitive training data through targeted prompting and evaluation of unexpected memorization.

Inference Attack Resistance: Evaluate resistance to attempts to extract potentially sensitive information about the model's training data through carefully crafted queries.

Data Minimization: Assess the model's tendency to generate or request unnecessary personal information for tasks that don't require such details.

Confidentiality Maintenance: Test how well the model maintains appropriate confidentiality boundaries in role-playing scenarios or when presented with information marked as confidential.

Evaluation Criteria Definition: Establish clear, measurable criteria for human evaluators to apply consistently, including detailed rubrics with examples for each scoring level.

Blind Comparison Protocols: Implement procedures for blind A/B testing between models or versions to reduce bias, including randomized response presentation and identifier removal.

Calibration Processes: Develop evaluator calibration procedures to ensure consistent application of criteria across different raters and over time.

Sampling Strategies: Design representative sampling approaches across task types, difficulty levels, and edge cases to provide comprehensive coverage of model capabilities.

Longitudinal Assessment: Establish protocols for consistent evaluation over time to track performance evolution through model versions and training iterations.

Evaluator Diversity: Recruit diverse evaluator pools across demographic, cultural, and expertise dimensions to capture varied perspectives on model outputs.

Domain Expert Integration: Incorporate domain specialists for evaluating performance in technical areas like medicine, law, programming, or specific scientific fields.

Training Program Development: Design comprehensive evaluator training programs with practice examples, calibration exercises, and feedback mechanisms.

Inter-Rater Reliability Measurement: Implement methods to measure and improve agreement between evaluators, including statistical measures like Cohen's Kappa or Fleiss' Kappa.

Expertise Layering: Structure evaluation to utilize different expertise levels appropriately, from crowd workers for basic assessments to domain experts for specialized evaluation.

Direct Assessment: Implement absolute quality rating scales for individual responses based on defined criteria like correctness, helpfulness, and safety.

Comparative Evaluation: Use side-by-side comparison methods where evaluators choose between outputs from different models for the same input.

Turing Test Variants: Apply modified Turing test approaches where evaluators distinguish between human-generated and AI-generated content for specific tasks.

Error Analysis: Conduct structured error categorization and root cause analysis for model mistakes to identify patterns and improvement opportunities.

User Simulation: Have evaluators role-play as different user types with varying needs, expertise levels, and communication styles to assess model adaptability.

Adversarial Testing: Train specialized evaluators to actively search for model weaknesses and edge cases through systematic prompting strategies.

Long-Form Content Assessment: Develop methodologies for evaluating extended model outputs like essays, stories, or multi-turn conversations that require holistic judgment.

Creative Content Evaluation: Establish subjective but consistent evaluation approaches for creative tasks like writing, idea generation, and artistic direction.

Reasoning Transparency: Assess the quality and transparency of the model's reasoning process, not just its final answers, particularly for complex problem-solving tasks.

Application-Specific Evaluation: Design specialized evaluation protocols that simulate real application contexts, complete with realistic constraints and requirements.

Automated Evaluation Pipelines: Design and implement automated pipelines that can execute standard benchmarks, generate reports, and track performance over time.

Evaluation Databases: Build structured databases to store evaluation results, enabling longitudinal analysis, correlation studies, and comparison across models and versions.

Interactive Dashboards: Develop visualization tools and dashboards that make evaluation results accessible and actionable for different stakeholders across the organization.

Continuous Evaluation Systems: Implement systems for ongoing evaluation throughout the development and deployment lifecycle, including automated regression testing.

Integrated Development Environments: Create specialized development environments that incorporate real-time evaluation feedback during model development and fine-tuning.

Evaluation Governance: Establish clear governance structures for evaluation processes, including ownership, approval workflows, and decision criteria based on evaluation results.

Cross-Functional Collaboration: Design collaboration models that engage product, engineering, research, legal, and ethics teams in the evaluation process and interpretation of results.

Milestone Definition: Define clear evaluation milestones and performance thresholds for different stages of the development and deployment process.

Training and Documentation: Develop comprehensive training materials and documentation to build evaluation literacy across the organization.

Feedback Loops: Implement structured feedback mechanisms that connect evaluation insights to model improvement initiatives and development priorities.

Resource Allocation: Develop frameworks for optimal resource allocation across different evaluation types, balancing automated benchmarks, human evaluation, and specialized assessments.

Cost-Benefit Analysis: Implement methods to assess the return on investment for different evaluation approaches, identifying high-value evaluation activities.

Scaling Strategies: Design strategies for scaling evaluation processes as model complexity and deployment scope increase, including sampling approaches and prioritization frameworks.

Efficiency Optimization: Identify opportunities to improve evaluation efficiency through automation, better tooling, and streamlined processes without sacrificing quality.

Risk-Based Prioritization: Develop risk assessment frameworks that prioritize evaluation resources toward areas with highest potential impact or liability.

Meta-Evaluation: Implement processes to evaluate the evaluation system itself, including accuracy, predictive validity, and correlation with real-world performance.

Benchmark Evolution: Establish procedures for regularly updating and expanding benchmark suites to address emerging capabilities and newly identified weaknesses.

Research Integration: Create mechanisms to rapidly incorporate new evaluation research and methodologies into operational evaluation frameworks.

Community Engagement: Develop approaches for engaging with broader AI evaluation communities, including participation in shared benchmarks and evaluation standards.

Adaptation Protocols: Establish clear protocols for adapting evaluation frameworks as models evolve and new capabilities or risks emerge in the field.

Emergent Capabilities: Develop evaluation approaches for increasingly complex emergent capabilities that weren't explicitly trained for, including advanced reasoning and creative problem-solving.

Multimodal Expansion: Anticipate evaluation needs for expanded multimodal capabilities beyond text and images, including video, audio, interactive environments, and cross-modal reasoning.

Tool Use Sophistication: Prepare for evaluating increasingly sophisticated tool use and function calling capabilities, including autonomous operation and complex workflow completion.

Long-Term Memory: Design evaluation methodologies for models with extended memory or persistent knowledge bases that evolve through continued interaction.

Simulation Capabilities: Develop approaches for evaluating model performance in simulation environments that test causal understanding and decision-making over time.

Interpretability Integration: Explore integration of model interpretability techniques into evaluation processes to assess not just performance but understanding of how results are produced.

Synthetic Evaluator Models: Research the potential and limitations of using specialized AI models as evaluation tools, particularly for first-pass or scalable assessment.

Predictive Evaluation: Develop techniques to predict real-world performance from benchmark results, strengthening the connection between controlled evaluations and practical applications.

Personalization Assessment: Create methodologies for evaluating personalized models and adaptation capabilities while maintaining evaluation consistency and comparability.

Cross-Model Transfer Assessment: Research approaches for evaluating how effectively capabilities transfer across model types, sizes, and architectural differences.

Industry Standards Development: Participate in and monitor the development of industry-wide evaluation standards, certification frameworks, and best practices.

Regulatory Alignment: Anticipate regulatory requirements for model evaluation and documentation, including potential mandatory testing in high-risk application areas.

Open Benchmarking Platforms: Support the development of open, collaborative benchmarking platforms that enable consistent cross-organization comparison and reduce duplication of effort.

Shared Evaluation Resources: Contribute to community efforts to create shared evaluation datasets, protocols, and tools that advance the field as a whole.

Evaluation Transparency: Prepare for increasing expectations around transparency in evaluation methods, including potential requirements to publish comprehensive evaluation results.

Cognitive Science Integration: Incorporate insights from cognitive science and human psychology into evaluation frameworks, particularly for assessing human-like reasoning and communication.

Sociotechnical Evaluation: Expand evaluation beyond technical performance to include sociotechnical dimensions of how models function within broader social and organizational systems.

Ethics and Philosophy: Engage with ethical frameworks and philosophical perspectives to better evaluate alignment with human values and societal benefit.

Human-Computer Interaction: Apply HCI research methods to evaluation processes, particularly for assessing usability, user experience, and effective human-AI collaboration.

Domain-Specific Methodologies: Develop specialized evaluation methodologies adapted from various professional domains like medicine, law, education, and creative industries that have established quality assessment approaches.