stindardlogic/instruction-following-hard-sft-100k

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