language:
- en
license: apache-2.0
size_categories: - 100K<n<1M
task_categories: - text-generation
pretty_name: Hard Instruction Following SFT (100K)
tags: - instruction-following
- multi-constraint
- sft
- supervised-fine-tuning
- format-following
- ifeval
- constraint-satisfaction
- synthetic
configs: - config_name: default
data_files:- split: train
path: instruction-following-hard-sft-100k.jsonl
- split: train
Hard Instruction Following SFT (100K)
100,000 ShareGPT conversations where the assistant correctly satisfies multiple simultaneous explicit constraints in a single response. Each example pairs a multi-constraint prompt with a response that honors every constraint without dropping any.
Targets the instruction-following capability measured by IFEval and similar benchmarks.
Motivation
A key failure mode in deployed LLMs is dropping constraints under load — responding to a multi-requirement prompt by satisfying some requirements while silently ignoring others. When a user says "write exactly 3 bullet points, each under 15 words, professional tone, mention X, don't mention Y," models often:
- Write 4 or 5 bullets when told exactly 3
- Exceed the word limit on some bullets
- Forget the exclusion constraint
- Drift from the specified tone
This dataset trains models to track and honor all constraints simultaneously.
Dataset Description
100,000 conversations across 21 prompt scenarios, each with 4-6 simultaneous constraints:
Constraint Count Distribution
| Constraints per prompt | Count | % |
|---|---|---|
| 4 | ~31,800 | 32% |
| 5 | ~50,000 | 50% |
| 6 | ~18,200 | 18% |
Constraint Types Covered
| Type | Description |
|---|---|
format |
Output structure (bullets, table, numbered list, prose) |
length |
Exact or maximum word/sentence/item counts |
tone |
Register (formal, casual, professional, academic, motivating) |
content |
Specific topics or elements that must appear |
exclusion |
Specific words, topics, or elements that must NOT appear |
structure |
Internal organization (SBI format, chronological, paragraph role) |
style |
Voice, persona, or rhetorical approach |
order |
Sequence constraints (X must come first, end with Y) |
audience |
Target reader expertise level |
persona |
Signature, role, or POV requirements |
Task Domains
Product writing, email drafting, code explanation, educational content, social media, academic writing, performance reviews, technical documentation, meeting facilitation, recipe/how-to guides, creative writing.
Format
{
"conversations": [
{
"from": "human",
"value": "Write a product description for a standing desk. Requirements: exactly 3 bullet points, each bullet under 15 words, professional tone, mention adjustable height and ergonomics, do NOT mention price or any specific brand name."
},
{
"from": "gpt",
"value": "- Adjustable height lets you alternate between sitting and standing throughout your workday.\n- Ergonomic design reduces back strain and promotes healthier posture over long hours.\n- Smooth, quiet motor enables quick transitions so you stay focused and productive."
}
],
"metadata": {
"constraint_count": 5,
"constraint_types": ["format", "length", "tone", "content", "exclusion"]
},
"id": "abc123"
}
Key Properties of Correct Responses
1. Complete constraint satisfaction: Every constraint in the prompt is honored in the response. No silent omissions.
2. Verification-friendly outputs: Responses are structured so constraints can be audited — exact bullet counts, verifiable word counts, no excluded terms.
3. Quality within constraints: Responses don't just technically satisfy constraints; they produce good outputs within the constraints. A 3-bullet product description should actually be a good product description.
4. Exclusion constraints strictly followed: "Do not use the word X" constraints are taken literally — the excluded words do not appear anywhere in the response.
5. Numeric constraints precise: "Exactly 4 sentences" means 4, not 3 or 5. "Under 15 words" means the bullet must count to 14 or fewer.
Relationship to IFEval
The IFEval benchmark (Zhou et al., 2023) measures instruction-following on prompts with verifiable constraints. The constraint types in this dataset directly correspond to IFEval's constraint categories:
- Length constraints (word count, sentence count, paragraph count)
- Format constraints (JSON, markdown, bullet lists)
- Content constraints (keyword inclusion/exclusion)
- Style constraints (language register, tone)
Training on this dataset should improve scores on IFEval and similar instruction-following evaluations.
Use Cases
- SFT fine-tuning for improved multi-constraint instruction following
- Training data for models targeting IFEval benchmark improvement
- Building AI writing assistants that reliably follow format specifications
- Enterprise AI deployment where output format consistency is critical
- Evaluation dataset development for constraint satisfaction testing
- Complementary to
instruction-following-dpo-100k(DPO pairs for same capability)
License
Apache 2.0