{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"ifeval","slug":"ifeval","name":"IFEval","type":"benchmark","url":"https://github.com/google-research/google-research/tree/master/instruction_following_eval","page_url":"https://unfragile.ai/ifeval","categories":["testing-quality"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"ifeval__cap_0","uri":"capability://safety.moderation.constraint.based.instruction.following.evaluation","name":"constraint-based instruction following evaluation","description":"Evaluates whether LLM-generated text adheres to verifiable formatting and structural constraints by parsing output against a rule-based constraint specification system. IFEval implements constraint checkers that validate word count limits, keyword inclusion/exclusion, punctuation requirements, capitalization patterns, and structural formatting (bullet points, numbered lists, paragraphs) through deterministic string matching and regex-based pattern validation rather than semantic evaluation.","intents":["Measure whether my LLM can follow explicit formatting instructions like 'respond in exactly 3 bullet points' or 'use no more than 50 words'","Benchmark instruction-following capability across different model architectures and sizes to identify which models respect hard constraints","Identify failure modes where models generate semantically correct but structurally non-compliant outputs","Compare instruction-following performance before and after fine-tuning or RLHF to validate training effectiveness"],"best_for":["LLM researchers evaluating model instruction-following capabilities","Teams fine-tuning models for constraint-aware generation","Benchmark maintainers building comprehensive LLM evaluation suites","Organizations requiring deterministic output formatting for downstream processing"],"limitations":["Only evaluates surface-level formatting constraints, not semantic instruction adherence or factual correctness","Constraint checkers are rule-based and brittle — cannot handle paraphrased or creatively-formatted compliance (e.g., 'here are my points:' instead of bullet points)","No evaluation of instruction comprehension or reasoning — only output format validation","Requires explicit constraint specification in structured format; cannot infer implicit formatting requirements from natural language instructions","Limited to English; constraint patterns may not generalize across languages with different punctuation or formatting conventions"],"requires":["Python 3.6+","Access to LLM outputs as text strings","Structured constraint definitions in IFEval format (JSON or Python objects)","No external API keys or model access required — purely offline evaluation"],"input_types":["LLM-generated text output","Constraint specification (structured rules defining formatting requirements)","Instruction prompts (optional, for reference)"],"output_types":["Boolean pass/fail per constraint","Aggregate compliance score (percentage of constraints satisfied)","Detailed constraint violation reports with specific failures"],"categories":["safety-moderation","testing-quality","benchmark"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ifeval__cap_1","uri":"capability://data.processing.analysis.multi.constraint.composition.and.weighting","name":"multi-constraint composition and weighting","description":"Enables evaluation of complex instruction sets by composing multiple formatting constraints into a single evaluation task with optional per-constraint weighting. The system supports AND/OR logic for constraint combinations, allowing evaluation of instructions like 'respond in bullet points AND use fewer than 100 words AND include the word X' by validating all constraints and aggregating results with configurable weights.","intents":["Evaluate realistic multi-constraint instructions that combine formatting, length, and content requirements","Weight certain constraints as more critical (e.g., safety-critical keywords must be present, but word count is flexible)","Identify which constraint combinations are hardest for models to satisfy simultaneously","Create custom evaluation rubrics where different formatting rules have different importance"],"best_for":["Researchers studying constraint interaction effects in instruction following","Teams building production LLM systems with multiple formatting requirements","Benchmark designers creating realistic multi-constraint evaluation scenarios"],"limitations":["Constraint interactions are evaluated independently — no detection of conflicting constraints (e.g., 'use exactly 10 words' AND 'write a detailed explanation')","Weighting is static and must be pre-defined; no adaptive weighting based on constraint difficulty or importance","No support for conditional constraints (e.g., 'if response length > 100 words, then must use bullet points')"],"requires":["Python 3.6+","Constraint definitions with optional weight parameters","Aggregation strategy specification (weighted sum, all-or-nothing, etc.)"],"input_types":["Multiple constraint specifications","Weight assignments per constraint (optional)","LLM output text"],"output_types":["Per-constraint pass/fail status","Weighted aggregate compliance score","Constraint satisfaction matrix"],"categories":["data-processing-analysis","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ifeval__cap_10","uri":"capability://tool.use.integration.constraint.extensibility.and.custom.constraint.definition","name":"constraint extensibility and custom constraint definition","description":"Allows users to define custom constraint types beyond the built-in validators by implementing constraint checker functions that follow the IFEval constraint interface. Custom constraints can be registered with the evaluation system and used in instruction-constraint pairs, enabling evaluation of domain-specific or novel constraint types.","intents":["Define custom constraints for domain-specific formatting requirements (e.g., medical terminology, legal document structure)","Implement novel constraint types not covered by built-in validators","Extend IFEval for specialized instruction-following evaluation","Create organization-specific constraint sets for internal evaluation"],"best_for":["Researchers extending IFEval for specialized domains","Organizations with custom formatting or content requirements","Teams building domain-specific instruction-following benchmarks","Advanced users needing constraint types beyond the standard set"],"limitations":["Custom constraint implementation requires Python programming; no declarative constraint definition language","Custom constraints must follow IFEval's constraint interface; incompatible implementations will fail silently","No built-in testing framework for custom constraints; users must write their own tests","Custom constraints are not automatically integrated with batch evaluation; may require additional configuration","Documentation for constraint interface may be sparse; reverse-engineering from built-in constraints may be necessary"],"requires":["Python 3.6+","IFEval framework source code or API documentation","Python programming knowledge","Understanding of IFEval's constraint interface and evaluation flow"],"input_types":["Custom constraint implementation (Python function or class)","Constraint configuration parameters","Test cases for validation"],"output_types":["Registered custom constraint","Constraint evaluation results","Integration with batch evaluation pipeline"],"categories":["tool-use-integration","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ifeval__cap_2","uri":"capability://data.processing.analysis.word.count.and.length.constraint.validation","name":"word count and length constraint validation","description":"Validates that LLM outputs conform to word count limits and length specifications by tokenizing output text and comparing against minimum/maximum word count thresholds. Implements configurable tokenization strategies (whitespace-based, punctuation-aware) to handle edge cases like contractions, hyphenated words, and punctuation attachment.","intents":["Verify that model responses respect 'respond in fewer than X words' constraints","Ensure generated content meets minimum length requirements for completeness","Detect models that pad responses with filler content to reach length targets","Benchmark length-constraint compliance across model sizes and architectures"],"best_for":["Evaluating models for applications requiring strict response length limits (e.g., social media, SMS, constrained UI)","Detecting length-padding behavior in fine-tuned models","Measuring instruction-following on length-based constraints"],"limitations":["Tokenization is language-dependent and may miscount words in languages with different word boundaries (e.g., Chinese, Japanese)","Does not account for semantic content density — a 50-word response may be more or less informative than another 50-word response","Whitespace-based tokenization treats contractions and hyphenated words inconsistently across different text preprocessing approaches","No distinction between 'exactly N words' vs 'at most N words' vs 'at least N words' in some implementations"],"requires":["Python 3.6+","Word count threshold specification (min/max)","Tokenization strategy selection"],"input_types":["LLM output text","Word count constraints (minimum and/or maximum)"],"output_types":["Boolean pass/fail","Actual word count","Constraint violation details (e.g., 'exceeded by 15 words')"],"categories":["data-processing-analysis","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ifeval__cap_3","uri":"capability://data.processing.analysis.keyword.inclusion.and.exclusion.constraint.checking","name":"keyword inclusion and exclusion constraint checking","description":"Validates that LLM outputs contain or exclude specific keywords and phrases by performing case-sensitive/insensitive substring matching and optional stemming/lemmatization. Supports both required keywords (must appear) and forbidden keywords (must not appear), with configurable matching strategies for handling variations like plurals, verb tenses, and word-form derivatives.","intents":["Verify that model responses include required domain-specific terminology or safety-critical keywords","Ensure models avoid prohibited terms, jargon, or sensitive language","Measure compliance with 'must mention X' or 'avoid saying Y' instructions","Detect whether models understand keyword-based constraints vs. semantic equivalence"],"best_for":["Safety-critical applications requiring specific terminology (e.g., 'must include disclaimer')","Domain-specific evaluation where certain terms must be present","Content moderation scenarios requiring absence of prohibited terms","Instruction-following benchmarks with keyword-based constraints"],"limitations":["Substring matching is brittle — 'contain' in 'container' matches 'contain' keyword, causing false positives","No semantic understanding — 'avoid negative language' cannot be validated, only specific words can be checked","Case sensitivity/insensitivity must be pre-configured; no adaptive handling of proper nouns vs. common words","Stemming/lemmatization adds complexity and may introduce false matches (e.g., 'running' and 'run' are related but 'runner' may not be intended)","No support for phrase-level constraints that require keywords in specific order or proximity"],"requires":["Python 3.6+","Keyword lists (required and/or forbidden)","Matching strategy configuration (case-sensitive, stemming, etc.)"],"input_types":["LLM output text","Required keywords list","Forbidden keywords list","Matching strategy parameters"],"output_types":["Boolean pass/fail per keyword","List of missing required keywords","List of forbidden keywords found","Aggregate keyword compliance score"],"categories":["data-processing-analysis","safety-moderation","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ifeval__cap_4","uri":"capability://data.processing.analysis.punctuation.and.capitalization.constraint.validation","name":"punctuation and capitalization constraint validation","description":"Validates formatting constraints related to punctuation usage and capitalization patterns by analyzing character-level properties of output text. Checks for requirements like 'must end with period', 'no exclamation marks', 'capitalize first letter of each sentence', or 'use title case' through pattern matching and character-level analysis.","intents":["Verify that responses follow standard punctuation conventions (e.g., must end with period)","Enforce capitalization rules (e.g., capitalize proper nouns, title case for headers)","Detect and validate specific punctuation requirements (e.g., 'no exclamation marks', 'use semicolons')","Measure compliance with formal writing style constraints"],"best_for":["Evaluating models for formal writing tasks with strict punctuation/capitalization rules","Content generation systems requiring consistent style (e.g., documentation, technical writing)","Instruction-following benchmarks with style-based constraints"],"limitations":["Capitalization rules are language-specific and may not generalize across languages with different case systems","No semantic understanding of when punctuation is appropriate (e.g., 'no exclamation marks' may be violated in quoted dialogue)","Sentence boundary detection is heuristic-based and may fail on abbreviations, ellipses, or non-standard formatting","Title case validation is ambiguous (e.g., should articles be capitalized in titles?) and language-dependent","No support for context-dependent punctuation rules (e.g., 'use commas in lists but not in inline text')"],"requires":["Python 3.6+","Punctuation/capitalization constraint specifications","Optional language specification for language-specific rules"],"input_types":["LLM output text","Punctuation constraints (e.g., 'must end with period', 'no exclamation marks')","Capitalization constraints (e.g., 'title case', 'sentence case')"],"output_types":["Boolean pass/fail per constraint","Specific punctuation/capitalization violations found","Aggregate style compliance score"],"categories":["data-processing-analysis","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ifeval__cap_5","uri":"capability://data.processing.analysis.structural.format.constraint.validation","name":"structural format constraint validation","description":"Validates that LLM outputs conform to specific structural formatting requirements like bullet points, numbered lists, paragraph structure, or table format by parsing output structure and matching against expected format patterns. Implements format detectors that identify list markers, indentation patterns, and structural delimiters to verify compliance with 'respond in bullet points' or 'use numbered list' constraints.","intents":["Verify that responses use required structural formats (bullet points, numbered lists, paragraphs)","Ensure models can follow 'format as a table' or 'use heading hierarchy' instructions","Detect whether models understand structural constraints vs. just including content","Measure compliance with document structure requirements"],"best_for":["Evaluating models for structured content generation (documentation, outlines, reports)","Instruction-following benchmarks with format-based constraints","Systems requiring specific output structures for downstream processing"],"limitations":["Format detection is heuristic-based and may fail on non-standard formatting (e.g., dashes instead of bullets, inconsistent indentation)","No semantic validation of structure — a bullet-point list with one item may technically pass but be semantically incomplete","Nested structure validation is complex and may have false negatives (e.g., sub-bullets not detected)","Markdown vs. plain-text format detection requires pre-specification; no automatic format inference","No validation of structural consistency (e.g., all bullet points should have similar content depth)"],"requires":["Python 3.6+","Structural format specification (bullet points, numbered list, paragraph, table, etc.)","Optional format-specific parameters (e.g., bullet character, indentation level)"],"input_types":["LLM output text","Structural format requirements","Optional format-specific parameters"],"output_types":["Boolean pass/fail","Detected structural format","Specific structural violations (e.g., 'missing bullet points', 'inconsistent indentation')","Structural compliance details"],"categories":["data-processing-analysis","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ifeval__cap_6","uri":"capability://data.processing.analysis.benchmark.dataset.and.instruction.set.management","name":"benchmark dataset and instruction set management","description":"Provides a curated dataset of 541 instructions with associated constraints covering diverse instruction types (writing, analysis, formatting, reasoning) and constraint categories. The dataset is organized with instruction text, constraint specifications, and reference outputs, enabling systematic evaluation of instruction-following across a representative sample of real-world instruction types.","intents":["Evaluate models on a standardized, diverse set of instruction-following tasks","Compare model performance across different instruction types and constraint categories","Identify which instruction types or constraint combinations are most challenging","Benchmark instruction-following capability in a reproducible, comparable way"],"best_for":["LLM researchers benchmarking instruction-following across models","Teams comparing model versions or architectures on instruction-following","Benchmark maintainers building comprehensive evaluation suites","Organizations establishing baseline instruction-following performance"],"limitations":["Dataset is fixed at 541 instructions; no dynamic or adaptive instruction generation","Instructions are primarily English; limited coverage of non-English instruction-following","Instruction diversity is limited to categories covered in the dataset; may not represent all real-world instruction types","No instruction difficulty stratification; all instructions are treated equally in aggregate scoring","Dataset may become outdated as instruction-following capabilities improve; no mechanism for continuous dataset evolution"],"requires":["Python 3.6+","Access to IFEval dataset (included in repository)","No external data sources required"],"input_types":["Instruction text","LLM output for evaluation"],"output_types":["Per-instruction constraint compliance","Aggregate benchmark score","Performance breakdown by instruction type and constraint category"],"categories":["data-processing-analysis","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ifeval__cap_7","uri":"capability://data.processing.analysis.constraint.compliance.scoring.and.aggregation","name":"constraint compliance scoring and aggregation","description":"Computes aggregate instruction-following scores by evaluating all constraints for an instruction and aggregating results into a single compliance metric. Supports multiple aggregation strategies (all-or-nothing, weighted sum, per-constraint breakdown) to provide both fine-grained diagnostic information and high-level performance summaries.","intents":["Generate a single instruction-following score for model comparison and ranking","Provide per-constraint breakdowns to identify which constraint types models struggle with","Weight constraints by importance to reflect real-world priorities","Track instruction-following performance across model versions and training iterations"],"best_for":["Researchers comparing instruction-following performance across models","Teams tracking instruction-following improvements during fine-tuning","Benchmark maintainers reporting standardized instruction-following scores","Organizations establishing instruction-following performance baselines"],"limitations":["Aggregation strategy must be pre-defined; no adaptive scoring based on constraint difficulty","All-or-nothing scoring is harsh and may not reflect partial compliance (e.g., 95% word count compliance fails completely)","Weighted aggregation requires manual weight assignment; no principled method for determining optimal weights","No confidence intervals or statistical significance testing; scores are point estimates","Aggregation masks constraint-specific failures; a model with 50% compliance on half the constraints and 100% on the other half appears as 75% overall"],"requires":["Python 3.6+","Per-constraint compliance results","Aggregation strategy specification","Optional constraint weights"],"input_types":["Per-constraint pass/fail results","Optional constraint weights","Aggregation strategy parameters"],"output_types":["Aggregate compliance score (0-100%)","Per-constraint compliance breakdown","Constraint category performance summary","Instruction-type performance breakdown"],"categories":["data-processing-analysis","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ifeval__cap_8","uri":"capability://automation.workflow.batch.evaluation.and.result.reporting","name":"batch evaluation and result reporting","description":"Enables evaluation of multiple LLM outputs against the full instruction set with batch processing and structured result reporting. Processes multiple model outputs, computes constraint compliance for each instruction, aggregates results, and generates detailed reports with per-instruction and per-constraint breakdowns.","intents":["Evaluate a model on all 541 benchmark instructions and generate a comprehensive report","Compare instruction-following performance across multiple models in a single batch run","Generate detailed evaluation reports for publication or internal documentation","Export evaluation results in structured format for further analysis"],"best_for":["Researchers benchmarking models on the full IFEval dataset","Teams comparing multiple model versions or architectures","Benchmark maintainers generating official evaluation results","Organizations documenting model capabilities for stakeholders"],"limitations":["Batch processing requires all model outputs to be pre-generated; no streaming or online evaluation","Result reporting is fixed to predefined formats; limited customization of report structure","No statistical analysis (confidence intervals, significance tests) in standard reporting","Large-scale batch evaluation may require significant compute for generating outputs for all 541 instructions","No built-in visualization of results; reports are text-based or tabular"],"requires":["Python 3.6+","Pre-generated LLM outputs for all instructions","IFEval evaluation framework","Sufficient disk space for storing outputs and reports"],"input_types":["Model outputs (text files or structured data)","Instruction set with constraints","Optional report configuration parameters"],"output_types":["Aggregate benchmark score","Per-instruction compliance results","Per-constraint category performance","Detailed evaluation report (JSON, CSV, or text format)","Comparison tables for multiple models"],"categories":["automation-workflow","data-processing-analysis","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ifeval__cap_9","uri":"capability://automation.workflow.instruction.constraint.pair.validation.and.debugging","name":"instruction-constraint pair validation and debugging","description":"Provides tools for validating that instruction-constraint pairs are correctly specified and for debugging constraint evaluation failures. Includes constraint specification validation, test harness for running individual instructions, and detailed error reporting to help identify issues in constraint definitions or evaluation logic.","intents":["Validate that constraint specifications are syntactically correct and semantically sensible","Debug why a specific instruction-constraint pair is failing unexpectedly","Test new constraint definitions before adding them to the benchmark","Identify ambiguous or conflicting constraints that may cause evaluation issues"],"best_for":["Benchmark maintainers adding new instructions to IFEval","Researchers creating custom instruction-constraint sets","Teams debugging unexpected evaluation results","Organizations extending IFEval with domain-specific constraints"],"limitations":["Debugging tools are primarily command-line based; no interactive GUI for constraint testing","Validation is syntactic and basic semantic checking; no deep analysis of constraint feasibility","No automated detection of conflicting constraints (e.g., 'exactly 10 words' AND 'write a detailed explanation')","Error messages may be cryptic for complex constraint combinations","No support for constraint templates or reusable constraint patterns"],"requires":["Python 3.6+","IFEval framework","Constraint specification in correct format","Test instruction and expected output (optional)"],"input_types":["Constraint specification","Test instruction text","Test LLM output (optional)"],"output_types":["Validation results (pass/fail with error details)","Constraint evaluation trace","Specific constraint violations","Debugging information and suggestions"],"categories":["automation-workflow","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ifeval__headline","uri":"capability://testing.quality.instruction.following.evaluation.benchmark.for.llms","name":"instruction-following evaluation benchmark for llms","description":"IFEval is a benchmark designed to assess how well large language models (LLMs) can adhere to specific formatting constraints in generated text, such as word count limits and structural requirements.","intents":["best instruction-following benchmark","benchmark for evaluating LLM formatting adherence","instruction-following evaluation tools","how to test LLMs for formatting compliance","LLM evaluation metrics for instruction following"],"best_for":["evaluating LLMs","testing AI text generation"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["testing-quality"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":63,"verified":false,"data_access_risk":"high","permissions":["Python 3.6+","Access to LLM outputs as text strings","Structured constraint definitions in IFEval format (JSON or Python objects)","No external API keys or model access required — purely offline evaluation","Constraint definitions with optional weight parameters","Aggregation strategy specification (weighted sum, all-or-nothing, etc.)","IFEval framework source code or API documentation","Python programming knowledge","Understanding of IFEval's constraint interface and evaluation flow","Word count threshold specification (min/max)"],"failure_modes":["Only evaluates surface-level formatting constraints, not semantic instruction adherence or factual correctness","Constraint checkers are rule-based and brittle — cannot handle paraphrased or creatively-formatted compliance (e.g., 'here are my points:' instead of bullet points)","No evaluation of instruction comprehension or reasoning — only output format validation","Requires explicit constraint specification in structured format; cannot infer implicit formatting requirements from natural language instructions","Limited to English; constraint patterns may not generalize across languages with different punctuation or formatting conventions","Constraint interactions are evaluated independently — no detection of conflicting constraints (e.g., 'use exactly 10 words' AND 'write a detailed explanation')","Weighting is static and must be pre-defined; no adaptive weighting based on constraint difficulty or importance","No support for conditional constraints (e.g., 'if response length > 100 words, then must use bullet points')","Custom constraint implementation requires Python programming; no declarative constraint definition language","Custom constraints must follow IFEval's constraint interface; incompatible implementations will fail silently","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.35,"ecosystem":0.15,"match_graph":0.2,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.692Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=ifeval","compare_url":"https://unfragile.ai/compare?artifact=ifeval"}},"signature":"0Cl0HWEBnUQZUG+wnY0hN2QcN/tdPW8COrb8DCGmpTFUoO9+oMnTRY1hVhhInVFgswPL6Maylhqi1cksgUllDQ==","signedAt":"2026-06-23T08:22:34.394Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/ifeval","artifact":"https://unfragile.ai/ifeval","verify":"https://unfragile.ai/api/v1/verify?slug=ifeval","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}