xCodeEval ranks higher at 67/100 vs OpenAssistant Conversations (OASST) at 59/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | OpenAssistant Conversations (OASST) | xCodeEval |
|---|---|---|
| Type | Dataset | Benchmark |
| UnfragileRank | 59/100 | 67/100 |
| Adoption | 1 | 1 |
| Quality |
| 1 |
| 1 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 8 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Extracts complete conversation trees from 66,497 human-authored dialogues where each message can have multiple child responses, creating a directed acyclic graph (DAG) structure. The dataset preserves branching paths where volunteers provided alternative continuations at decision points, enabling training on diverse response distributions for the same context. This tree structure is serializable to JSON with parent-child message IDs, allowing downstream systems to reconstruct full conversation histories or sample specific branches for preference learning.
Unique: Preserves full conversation DAG with multiple child branches per message, unlike flat conversation datasets (e.g., ShareGPT) that linearize to single paths. Enables direct preference learning from sibling responses without synthetic pairing.
vs alternatives: Larger human-written branching dataset than alternatives like HH-RLHF (which uses synthetic preference pairs), allowing reward models to learn from natural human divergence rather than algorithmic ranking.
Each message includes quality ratings from multiple human annotators (typically 3-5 raters per message) on dimensions like helpfulness, harmlessness, and honesty. The dataset provides aggregated scores (mean, median, or consensus) plus raw per-annotator ratings, enabling calculation of inter-rater reliability (Krippendorff's alpha, Fleiss' kappa) and identification of ambiguous examples. This multi-rater approach reduces individual bias and allows filtering by agreement threshold to create high-confidence training subsets.
Unique: Provides raw per-annotator ratings alongside aggregates, enabling downstream systems to compute custom agreement metrics and weight examples by confidence rather than using fixed aggregation. Most datasets only expose final scores.
vs alternatives: Richer annotation metadata than single-rater datasets (e.g., Alpaca) or datasets with binary labels, allowing nuanced quality-based filtering and confidence-weighted training.
Messages are annotated with toxicity scores and categorical safety labels (e.g., sexual content, violence, illegal activity, misinformation) applied by human annotators. The dataset exposes both binary flags (toxic/non-toxic) and continuous toxicity scores, plus detailed category breakdowns. This enables training safety classifiers, filtering harmful content, and analyzing the distribution of safety issues across conversation types and languages.
Unique: Multi-dimensional safety annotations (toxicity score + categorical labels) across 35 languages, rather than single binary toxic/non-toxic flags. Enables language-specific and category-specific safety filtering.
vs alternatives: More comprehensive safety metadata than generic instruction datasets (e.g., Alpaca), and covers low-resource languages beyond English-centric datasets like HH-RLHF.
Contains 161,443 messages across 35 languages with uneven distribution (English-dominant but includes low-resource languages like Swahili, Vietnamese, Polish). The dataset structure allows filtering by language code and sampling balanced subsets across languages. This enables training multilingual models, analyzing language-specific conversation patterns, and studying how human preferences vary across linguistic and cultural contexts.
Unique: Covers 35 languages including low-resource ones (Swahili, Vietnamese, Polish) with human-written conversations, not machine-translated. Enables genuine cross-lingual preference learning rather than synthetic translation.
vs alternatives: Broader language coverage than English-centric datasets (e.g., ShareGPT, HH-RLHF), though with language imbalance requiring careful sampling. Larger low-resource language component than most instruction datasets.
Automatically generates preference training pairs by comparing sibling responses (multiple continuations of the same prompt) using aggregated human quality ratings. For each prompt with N child responses, the system creates preference triplets (prompt, higher-rated_response, lower-rated_response) by ranking children by quality score. This avoids synthetic preference generation and grounds preference learning in actual human judgments, enabling direct training of reward models and DPO-style algorithms.
Unique: Derives preferences from natural conversation branching and human ratings rather than synthetic comparison or LLM-based ranking. Grounds preference learning in actual human judgments without additional annotation.
vs alternatives: More authentic preference signal than synthetic pairs (e.g., GPT-4 ranking) or single-response datasets. Enables preference learning at scale without expensive pairwise human annotation.
Flattens conversation trees into instruction-response pairs by treating each user message as an instruction and the following assistant message as the response. Handles multi-turn context by optionally including conversation history or using only the immediate prompt-response pair. This enables straightforward supervised fine-tuning (SFT) of language models without requiring preference learning infrastructure, suitable for baseline model training or quick prototyping.
Unique: Preserves conversation tree structure while enabling flat pair extraction, allowing users to choose between SFT (flat pairs) and preference learning (branching) without data duplication.
vs alternatives: More flexible than single-format datasets — supports both SFT and preference learning from the same source, vs datasets optimized for only one approach.
Each conversation includes metadata tags or inferred categories (e.g., creative writing, coding, Q&A, general knowledge) enabling domain-specific filtering and analysis. While not explicitly documented as structured tags in the original dataset, the message content and conversation structure allow downstream systems to classify conversations by type. This enables creating domain-specific training subsets, analyzing model performance across task types, and studying how human preferences vary by domain.
Unique: Conversation diversity (creative writing, coding, Q&A, general knowledge) within a single dataset enables domain-specific analysis and filtering, though without explicit labels requiring custom classification.
vs alternatives: Broader task coverage than single-domain datasets (e.g., code-specific or creative writing-specific), allowing multi-domain model training or domain-specific subset creation.
161,443 messages collected from 13,000+ volunteer annotators through a crowdsourced platform (Open Assistant project), not generated by LLMs or synthetic methods. The annotation pipeline includes message creation, quality rating, toxicity labeling, and ranking by multiple independent raters. This human-centric approach ensures authentic conversational patterns, diverse writing styles, and genuine human preferences, though with inherent quality variance across annotators.
Unique: Largest human-written (not LLM-generated) instruction dataset at scale, created by 13,000+ volunteers rather than single-model generation or synthetic methods. Preserves natural human diversity in writing and preferences.
vs alternatives: More authentic and diverse than LLM-generated datasets (e.g., Alpaca, ShareGPT based on ChatGPT) or synthetic preference pairs. Larger human-written component than most alternatives, though with quality variance requiring filtering.
Provides a standardized evaluation framework for code generation models that accepts generated code in 17 programming languages (C, C++, C#, Java, Kotlin, Go, Rust, Python, Ruby, PHP, JavaScript, Perl, Haskell, OCaml, Scala, D, Pascal) and validates correctness through actual execution against unit tests via the ExecEval Docker-based execution engine. Uses a centralized problem definition model with src_uid foreign keys linking generated code to shared problem descriptions and unittest_db.json, enabling consistent evaluation across language variants of the same problem.
Unique: Combines 25M training examples across 7,500 unique problems with an execution-based evaluation pipeline (ExecEval) that actually runs generated code in Docker containers against unit tests, rather than relying on static analysis or string matching. The src_uid linking system creates a normalized data model where problem descriptions and tests are stored once and referenced by all language variants, eliminating duplication and ensuring consistency.
vs alternatives: Larger scale (25M examples vs typical 10-100K) and true execution-based validation across more languages (17 vs 4-6) than HumanEval or CodeXGLUE, with explicit support for code translation and repair tasks beyond generation.
Implements a foreign key linking system where all task-specific datasets (program synthesis, code translation, APR, retrieval) reference shared problem definitions via src_uid identifiers. Problem descriptions and unit tests are stored once in centralized problem_descriptions.jsonl and unittest_db.json files, then linked by src_uid to avoid duplication. The Hugging Face datasets API automatically resolves these links during data loading, returning enriched DatasetDict objects with problem context pre-joined to task examples.
Unique: Uses a normalized relational data model (src_uid as foreign key) for a code benchmark, treating problem definitions as a separate entity layer rather than embedding them in each task dataset. This is more sophisticated than typical flat-file benchmark structures and enables consistent multi-task evaluation on identical problems.
xCodeEval scores higher at 67/100 vs OpenAssistant Conversations (OASST) at 59/100.
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vs alternatives: More efficient than duplicating problem descriptions across 7 task datasets (reduces storage by ~30-40%), and enables automatic link resolution via Hugging Face API unlike manual CSV joins in CodeXGLUE or HumanEval variants.
Provides a Python API for loading xCodeEval datasets from Hugging Face Hub (NTU-NLP-sg/xCodeEval) with automatic src_uid-based linking between task datasets and shared problem definitions. The datasets library handles data downloading, caching, and streaming, while the xCodeEval integration automatically joins task examples with problem_descriptions.jsonl and unittest_db.json using src_uid foreign keys. Returns DatasetDict objects with enriched examples ready for model training or evaluation.
Unique: Integrates xCodeEval with Hugging Face datasets library, providing automatic src_uid resolution and streaming support. Treats data loading as a first-class concern with built-in linking logic, rather than requiring manual JSON parsing.
vs alternatives: More convenient than manual Git LFS downloads because it handles caching and automatic linking, and integrates seamlessly with Hugging Face training pipelines vs custom data loaders.
Provides an alternative data access method using Git LFS for users who prefer direct file access or need selective dataset downloads. Supports cloning the repository with LFS disabled, then pulling specific task files or problem definitions on demand. Useful for custom processing pipelines or environments where Python/Hugging Face is not available, though requires manual src_uid linking to join task examples with problem definitions.
Unique: Provides Git LFS-based alternative to Hugging Face API, enabling direct file access and selective downloads. Requires manual src_uid linking but offers more control over data access patterns.
vs alternatives: More flexible than Hugging Face API for selective downloads and custom pipelines, but requires more manual work for src_uid linking and lacks automatic caching/streaming.
Implements a standardized three-phase evaluation pipeline (Phase 1: Generation, Phase 2: Execution, Phase 3: Metrics) that applies consistently across all 7 tasks (program synthesis, code translation, APR, tag classification, code compilation, NL-code retrieval, code-code retrieval). Phase 1 generates or retrieves code, Phase 2 executes it via ExecEval or computes retrieval metrics, and Phase 3 aggregates results into pass@k, MRR, NDCG, or other task-specific metrics. Enables direct comparison of model performance across tasks.
Unique: Defines a unified three-phase evaluation pipeline that applies to all 7 tasks, treating generation, execution, and metric computation as separate concerns. Enables consistent evaluation methodology across diverse task types (generation, translation, retrieval, classification).
vs alternatives: More comprehensive than task-specific evaluation scripts because it provides a unified framework for all 7 tasks, and enables direct comparison of model performance across different task types.
Evaluates code generation models on the program synthesis task by accepting natural language problem descriptions and generating code solutions in any of 17 languages. The evaluation pipeline (Phase 1: Generation, Phase 2: Execution, Phase 3: Metrics) runs generated code against unit tests via ExecEval, computing pass@k metrics (pass@1, pass@10, etc.) that measure the probability of finding a correct solution within k samples. Supports both single-solution and multi-sample evaluation modes for assessing model reliability.
Unique: Implements a three-phase evaluation pipeline (Generation → Execution → Metrics) with explicit pass@k computation that measures the probability of finding a correct solution within k attempts, rather than just binary pass/fail. Supports multi-sample evaluation across 17 languages with language-specific compiler configurations and timeout handling.
vs alternatives: More rigorous than HumanEval's simple pass@k because it handles language-specific compilation errors and timeouts explicitly, and scales to 25M training examples vs HumanEval's 164 problems.
Evaluates code translation models by accepting source code in one language and generated translations in a target language, then validating functional equivalence through execution against shared unit tests. The translation evaluation pipeline compiles and executes both source and translated code against the same unittest_db.json test cases, comparing outputs to detect translation errors. Supports all 17 language pairs (though not all pairs may have training data) and uses language-specific compiler mappings to handle syntax differences.
Unique: Validates code translation by executing both source and target code against identical unit tests and comparing outputs, ensuring functional equivalence rather than syntactic similarity. Uses language-specific compiler mappings to handle the complexity of 17 different compilation environments and their idiosyncrasies.
vs alternatives: More rigorous than BLEU-score-based translation metrics because it validates actual functional correctness through execution, and covers more language pairs (17 vs typical 2-4) with explicit compiler integration.
Evaluates program repair models by providing buggy code snippets and expecting corrected versions that pass unit tests. The APR evaluation pipeline executes repaired code against unittest_db.json test cases, measuring whether the repair successfully fixes the bug without introducing new failures. Supports repairs across all 17 languages and uses the same execution-based validation as program synthesis, enabling direct comparison of repair quality.
Unique: Treats program repair as an executable task where success is measured by unit test passage, rather than syntactic similarity to reference repairs. Integrates with the same ExecEval pipeline as program synthesis, enabling direct performance comparison between generation and repair models.
vs alternatives: More comprehensive than traditional APR benchmarks (Defects4J, QuixBugs) because it covers 17 languages and 7,500 problems vs 395 Java bugs, and uses consistent execution-based metrics across all repair types.
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