README.md

---
license: cc-by-sa-3.0
pretty_name: dolly 15k enriched
language:
- en
tags:
- databricks
- dolly
- NLP
- semantic
- llm-evaluation
- fine-tuning
- text-statistics
- sentence-transformers
- embedding
- faiss-compatible
---
## Databricks - Dolly 15k – Enriched Variant (Instruction-Tuned with Semantic and Complexity Augmentation)

Overview

This dataset is a semantically enriched and complexity-aware extension of the original Databricks Dolly 15k, purpose-built for evaluating and training instruction-following models. Each sample is augmented with additional signals to enable more nuanced filtering, curriculum learning, and benchmark development across diverse NLP tasks.

## Dataset Format

Each sample includes the following fields:

instruction (str) – the prompt or task instruction

context (str) – optional background text (can be empty)

response (str) – the ideal output corresponding to the instruction

category (str) – the original category label (e.g., closed_qa, generation)

category_enriched (List[str]) – multi-class enriched taxonomy based on LLM-based relabeling

embedding (List[float]) – 384-dimensional semantic representation using a SentenceTransformer model

instruction_readability (float) – Flesch Reading Ease score of the instruction

response_readability (float) – Flesch Reading Ease score of the response

instruction_tokens (int) – number of tokens in the instruction

response_tokens (int) – number of tokens in the response

## Enrichment Details

# 1. Semantic Embeddings (384-D)

Model: all-MiniLM-L6-v2 from SentenceTransformers

Purpose:

Enables vector search and similarity-based retrieval.

Facilitates clustering or curriculum grouping based on semantic distance.

Use Case: RAG pipelines, hybrid retriever-generator evaluation, semantic data deduplication.

# 2. Multi-Label Category Enrichment

Method: LLM-based enrichment of original category into multiple labels reflecting nuanced intent (e.g., closed_qa, classification, instruction_reformulation).

Purpose:

Allows for filtering and training multi-task models with fine-grained task type supervision.

Enables few-shot sampling or balanced evaluation subsets.

Use Case: Model generalization studies, task disambiguation training, LLM taxonomy alignment.

# 3. Readability Scores

Metric: Flesch Reading Ease

Range: Typically from -10 (very complex/short text) to 100+ (very easy to read)

Interpretation:

Higher is simpler: A score above 60 indicates easy-to-read content.

Negative values: Usually from one-word answers or very short instructions.

Purpose:

Measures linguistic complexity for curriculum learning.

Enables filtering of prompts based on difficulty level.

# 4. Token Lengths (Instruction/Response)

Method: tiktoken tokenizer for gpt-3.5-turbo vocabulary

Purpose:

Supports token-level curriculum filtering.

Enables outlier detection for unusually long or short samples.

Use Case: Model length conditioning, latency profiling, instruction tuning length analysis.

## Research Use Cases

Curriculum Learning: Use readability and token length to gradually train models from simple to complex examples.

Semantic Similarity Evaluation: Leverage embeddings for nearest-neighbor search, duplicate detection, or hybrid retriever training.

Task-Type Robustness: Train and evaluate models across enriched multi-label categories to assess generalization across QA, classification, and generative tasks.

Prompt Engineering Validation: Analyze impact of prompt complexity (via readability/tokens) on response quality.

## Citation (Original)

@online{DatabricksBlog2023DollyV2,
    author    = {Mike Conover and Matt Hayes and Ankit Mathur and Jianwei Xie and Jun Wan and Sam Shah and Ali Ghodsi and Patrick Wendell and Matei Zaharia and Reynold Xin},
    title     = {Free Dolly: Introducing the World's First Truly Open Instruction-Tuned LLM},
    year      = {2023},
    url       = {https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm},
    urldate   = {2023-06-30}
}

Wikipedia (various pages) - https://www.wikipedia.org/ Copyright © Wikipedia editors and contributors.

## License

Same as original Dolly 15k: Creative Commons Attribution-ShareAlike 3.0 (CC BY-SA 3.0)