{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"ultrafeedback","slug":"ultrafeedback","name":"UltraFeedback","type":"dataset","url":"https://huggingface.co/datasets/openbmb/UltraFeedback","page_url":"https://unfragile.ai/ultrafeedback","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"ultrafeedback__cap_0","uri":"capability://data.processing.analysis.multi.dimensional.preference.annotation.across.llm.responses","name":"multi-dimensional preference annotation across llm responses","description":"Provides 64K prompts with responses from multiple LLMs (GPT-3.5, GPT-4, Claude, Llama, etc.) annotated with preference judgments across four orthogonal dimensions: helpfulness, honesty, instruction-following, and truthfulness. Each prompt has multiple response pairs with comparative ratings, enabling fine-grained preference learning that captures nuanced trade-offs between model behaviors rather than single-axis ranking.","intents":["Train DPO or RLHF models with multi-objective preference signals instead of monolithic reward functions","Analyze which LLM behaviors correlate with human preferences across different evaluation axes","Create preference datasets that distinguish between helpful-but-dishonest vs honest-but-unhelpful responses","Benchmark how well models learn to balance competing objectives like instruction-following vs truthfulness"],"best_for":["ML teams training preference-based models (DPO, IPO, RLHF) who need multi-dimensional feedback signals","Researchers studying trade-offs between model alignment objectives","Organizations building domain-specific LLMs requiring nuanced preference data beyond binary helpfulness"],"limitations":["Annotations are English-only; no multilingual preference data for non-English model training","Preference judgments may reflect annotator biases in how they weight the four dimensions; no inter-annotator agreement statistics provided","Limited to 64K prompts; sparse coverage for specialized domains like medical, legal, or code-heavy tasks","No temporal metadata on when responses were generated; model versions and training data cutoffs may differ across response pairs","Annotations are static; no mechanism to update preferences as model capabilities evolve"],"requires":["Hugging Face Datasets library (datasets>=2.0)","Python 3.8+","Sufficient disk space (~15-20GB for full dataset with all response variants)","Understanding of preference learning frameworks (DPO, RLHF, or similar)"],"input_types":["text prompts (natural language instructions, questions, dialogue contexts)"],"output_types":["structured JSON with prompt, multiple LLM responses, and preference annotations","preference pairs (response_A, response_B, winner, dimension_scores)"],"categories":["data-processing-analysis","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ultrafeedback__cap_1","uri":"capability://data.processing.analysis.cross.model.response.comparison.dataset.construction","name":"cross-model response comparison dataset construction","description":"Systematically collects responses to identical prompts from 4+ diverse LLMs (GPT-3.5, GPT-4, Claude, Llama, etc.) with different architectures, training procedures, and capability profiles. Responses are paired and annotated to enable comparative analysis of how model families differ in their approach to the same task, supporting contrastive learning and model behavior analysis.","intents":["Train models to learn from comparative examples showing how different LLMs solve the same problem","Analyze systematic differences in how model families approach instruction-following, truthfulness, and helpfulness","Create synthetic preference data by comparing responses from weaker vs stronger models on identical prompts","Build datasets for model merging or ensemble methods that learn to combine strengths of multiple model families"],"best_for":["Researchers studying model behavior divergence and comparative capabilities","Teams training models via contrastive learning from multiple teacher models","Organizations building model selection or routing systems that need comparative performance data"],"limitations":["Response quality depends on model versions used; GPT-4 responses may be significantly better than Llama-7B, creating imbalanced preference data","No control for response generation parameters (temperature, top-p); different models may have been sampled with different hyperparameters","Responses are static snapshots; cannot track how model behavior changes with fine-tuning or instruction engineering","No metadata on which model generated which response in some splits, limiting contrastive learning applications"],"requires":["Hugging Face Datasets library","Python 3.8+","Familiarity with contrastive learning or preference-based training"],"input_types":["text prompts"],"output_types":["response tuples (prompt, response_from_model_A, response_from_model_B, response_from_model_C, ...)","preference annotations comparing responses"],"categories":["data-processing-analysis","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ultrafeedback__cap_2","uri":"capability://data.processing.analysis.dimension.specific.preference.filtering.and.stratification","name":"dimension-specific preference filtering and stratification","description":"Enables filtering and stratifying the 64K prompts by preference dimension (helpfulness, honesty, instruction-following, truthfulness) to create task-specific subsets where one dimension dominates. Supports extracting prompts where models disagree on a specific dimension while agreeing on others, enabling targeted training on particular behavioral objectives without confounding signals from other dimensions.","intents":["Create a training subset focused only on improving model honesty while holding other dimensions constant","Identify prompts where instruction-following and truthfulness are in tension, for studying trade-off learning","Build evaluation sets that isolate performance on a single dimension without confounding from others","Train specialized models optimized for specific objectives (e.g., a 'helpful-but-honest' variant vs 'maximally-helpful' variant)"],"best_for":["Teams training models with specific behavioral objectives (e.g., 'maximize honesty' or 'maximize instruction-following')","Researchers studying how models learn to balance competing objectives","Organizations building multiple model variants optimized for different use cases"],"limitations":["Dimension annotations may not be perfectly independent; a response rated high on 'honesty' might correlate with 'helpfulness' due to annotator bias","No quantitative dimension scores; only comparative preferences between response pairs, limiting fine-grained stratification","Filtering by dimension may create imbalanced subsets with very few examples for rare dimension combinations","No metadata on annotation confidence; cannot distinguish high-confidence from borderline dimension judgments"],"requires":["Hugging Face Datasets library with filtering/mapping support","Python 3.8+","Understanding of which dimension aligns with your training objective"],"input_types":["structured preference annotations with dimension labels"],"output_types":["filtered dataset subsets stratified by dimension","preference pairs grouped by dominant dimension"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ultrafeedback__cap_3","uri":"capability://data.processing.analysis.rlhf.and.dpo.training.data.formatting.and.serialization","name":"rlhf and dpo training data formatting and serialization","description":"Provides preference data in standardized formats compatible with RLHF and DPO training pipelines, including prompt-response pairs, preference rankings, and dimension-specific scores serialized as JSON or Parquet. Data is pre-processed to remove duplicates, handle edge cases (empty responses, encoding errors), and normalize formatting across different LLM outputs, reducing preprocessing overhead for training teams.","intents":["Load preference data directly into RLHF training scripts without custom parsing or format conversion","Train DPO models with minimal data preprocessing by using pre-formatted preference pairs","Integrate preference data into existing training pipelines (TRL, DeepSpeed, etc.) without custom ETL","Export subsets of preference data in formats compatible with specific training frameworks"],"best_for":["ML engineers implementing RLHF or DPO training who want to minimize data preprocessing","Teams using established training frameworks (TRL, DeepSpeed, Hugging Face Transformers) that expect standard formats","Organizations with limited data engineering resources who need ready-to-use training data"],"limitations":["Format is optimized for RLHF/DPO; may require custom transformation for other preference learning methods (IPO, KTO, etc.)","No built-in support for dynamic data augmentation or on-the-fly format conversion; static serialization only","Parquet format may be inefficient for streaming training on very large models; requires loading full dataset into memory","No versioning or schema validation; breaking changes to data format could affect downstream training pipelines"],"requires":["Hugging Face Datasets library","Python 3.8+","TRL, DeepSpeed, or similar training framework (optional but recommended)"],"input_types":["structured preference annotations"],"output_types":["JSON serialized preference pairs","Parquet files with prompt, responses, and preference labels","PyArrow tables compatible with Hugging Face Datasets"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ultrafeedback__cap_4","uri":"capability://data.processing.analysis.prompt.diversity.and.coverage.analysis","name":"prompt diversity and coverage analysis","description":"The 64K prompts span multiple task categories (writing, math, reasoning, coding, QA, etc.) with varying complexity levels and instruction styles. Enables analysis of how preference patterns differ across task types and complexity levels, supporting evaluation of whether trained models generalize across diverse task distributions or overfit to specific prompt characteristics.","intents":["Analyze whether preference patterns (e.g., which model is preferred) are consistent across task types or task-dependent","Evaluate whether models trained on this data generalize to out-of-distribution prompts or overfit to specific task characteristics","Create balanced training subsets that cover diverse task types equally, avoiding bias toward any single task category","Benchmark model performance across different prompt complexities to identify capability gaps"],"best_for":["Researchers studying generalization and task-specific preference patterns","Teams building models that need to perform well across diverse task types","Organizations evaluating whether preference data from one domain transfers to another"],"limitations":["Task category labels are not provided in the dataset; requires manual annotation or inference to stratify by task type","Prompt distribution may not be uniform across task types; some categories may be overrepresented","No metadata on prompt difficulty or complexity; cannot easily identify which prompts are 'hard' vs 'easy'","Coverage is limited to English prompts; no analysis of how preferences generalize across languages"],"requires":["Hugging Face Datasets library","Python 3.8+","Optional: task classification model or manual annotation for stratification"],"input_types":["text prompts"],"output_types":["task-stratified subsets","coverage analysis reports","complexity-based prompt groupings"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ultrafeedback__cap_5","uri":"capability://data.processing.analysis.response.quality.variance.quantification.across.model.families","name":"response quality variance quantification across model families","description":"Captures response quality variance by collecting responses from multiple LLMs with different capability levels (GPT-4 as high-quality baseline, GPT-3.5 and Claude as mid-tier, Llama as open-source baseline) to the same prompts. Enables quantification of how much response quality varies across models and identification of prompts where models diverge significantly, supporting analysis of model capability gaps and preference learning robustness.","intents":["Quantify how much response quality varies across different model families on the same task","Identify prompts where models disagree significantly, indicating areas of genuine difficulty or ambiguity","Evaluate whether preference learning methods are robust to variation in response quality or sensitive to specific model pairs","Create balanced preference pairs by matching responses of similar quality from different models"],"best_for":["Researchers studying model capability gaps and preference learning robustness","Teams evaluating whether trained models are robust to variation in response quality","Organizations analyzing which tasks are genuinely difficult vs which are easy for all models"],"limitations":["Response quality is inferred from preference annotations, not measured directly; no objective quality metrics provided","Quality variance may reflect model version differences rather than fundamental capability gaps","No control for response generation parameters; different models may have been sampled with different hyperparameters, confounding quality comparisons","Limited to 4-5 model families; cannot analyze variance across broader model ecosystem"],"requires":["Hugging Face Datasets library","Python 3.8+","Statistical analysis tools for variance quantification"],"input_types":["preference annotations with model identifiers"],"output_types":["variance statistics by model pair and task type","divergence metrics identifying high-disagreement prompts","quality-matched response pairs"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ultrafeedback__cap_6","uri":"capability://data.processing.analysis.annotation.consistency.and.inter.rater.agreement.analysis","name":"annotation consistency and inter-rater agreement analysis","description":"Preference annotations are provided with implicit consistency information through multiple response pairs per prompt and dimension-specific ratings. Enables analysis of annotation consistency by examining whether annotators agree on preference rankings across different response pairs from the same prompt, and whether dimension-specific ratings are internally consistent (e.g., does a response rated high on 'honesty' also score high on 'truthfulness').","intents":["Evaluate annotation quality and identify potentially mislabeled or ambiguous preference pairs","Analyze whether annotators have consistent preferences across different response pairs from the same prompt","Identify prompts or response types where annotators disagree significantly, indicating genuine ambiguity","Filter out low-confidence annotations before using data for training"],"best_for":["Data quality engineers validating preference annotations before training","Researchers studying annotation consistency and its impact on preference learning","Teams building robust preference learning systems that need to account for annotation uncertainty"],"limitations":["No explicit inter-rater agreement statistics provided; consistency must be inferred from response pair patterns","Single annotation per preference pair; no redundant annotations to measure agreement directly","No confidence scores or annotator metadata; cannot distinguish high-confidence from borderline judgments","Dimension-specific ratings may not be perfectly independent; correlation between dimensions could reflect annotator bias rather than genuine response properties"],"requires":["Hugging Face Datasets library","Python 3.8+","Statistical analysis tools for consistency measurement"],"input_types":["preference annotations with dimension labels"],"output_types":["consistency metrics by prompt and dimension","agreement statistics across response pairs","confidence-filtered annotation subsets"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ultrafeedback__cap_7","uri":"capability://data.processing.analysis.instruction.following.vs.truthfulness.trade.off.dataset","name":"instruction-following vs truthfulness trade-off dataset","description":"Explicitly captures prompts and responses where instruction-following and truthfulness are in tension (e.g., a prompt asking for false information, or requesting a response in a specific format that conflicts with accuracy). Enables training models to learn principled trade-offs between competing objectives rather than blindly optimizing for one dimension, supporting development of models that can balance competing goals.","intents":["Train models to recognize and navigate trade-offs between instruction-following and truthfulness","Evaluate whether models learn to prioritize truthfulness over blind instruction-following","Create evaluation sets that test models on their ability to handle conflicting objectives","Study how different training methods (RLHF, DPO, etc.) handle objective trade-offs"],"best_for":["Teams building models that need to balance competing objectives (e.g., helpful but honest)","Researchers studying how models learn to handle conflicting instructions","Organizations building safety-critical systems where truthfulness must be preserved even when it conflicts with instructions"],"limitations":["Trade-off prompts may be underrepresented in the dataset; no explicit filtering for trade-off scenarios","Annotation of trade-offs is implicit in dimension-specific ratings; no explicit metadata identifying which prompts involve trade-offs","No guidance on how to weight competing objectives; models must learn trade-off preferences from examples alone","Trade-off patterns may be specific to English prompts and instruction styles; generalization to other languages unclear"],"requires":["Hugging Face Datasets library","Python 3.8+","Understanding of multi-objective optimization and preference learning"],"input_types":["prompts with conflicting objectives","responses with dimension-specific ratings"],"output_types":["trade-off scenario subsets","dimension-specific preference pairs highlighting conflicts","trade-off analysis reports"],"categories":["data-processing-analysis","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ultrafeedback__headline","uri":"capability://model.training.large.scale.preference.dataset.for.llm.training","name":"large-scale preference dataset for llm training","description":"UltraFeedback is a comprehensive dataset designed for training language models, featuring 64K prompts rated across multiple dimensions to enhance RLHF and DPO methodologies.","intents":["best dataset for LLM training","preference dataset for reinforcement learning","datasets for model fine-tuning","large datasets for language model evaluation","datasets for RLHF training"],"best_for":["researchers in NLP","developers training LLMs"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["model-training"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":56,"verified":false,"data_access_risk":"low","permissions":["Hugging Face Datasets library (datasets>=2.0)","Python 3.8+","Sufficient disk space (~15-20GB for full dataset with all response variants)","Understanding of preference learning frameworks (DPO, RLHF, or similar)","Hugging Face Datasets library","Familiarity with contrastive learning or preference-based training","Hugging Face Datasets library with filtering/mapping support","Understanding of which dimension aligns with your training objective","TRL, DeepSpeed, or similar training framework (optional but recommended)","Optional: task classification model or manual annotation for stratification"],"failure_modes":["Annotations are English-only; 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