README.md

---
license: other
license_name: open-data-attribution-training-disclosure-license-odatl-1.0
license_link: LICENSE
language:
- in
tags:
- synthetic
---
# token_efficiency_corpus

A 2.5 GB CSV corpus teaching LLMs to minimize token usage in their outputs.
Progresses from basic filler removal to expert-level nested reasoning compression.

---

## Contents

  verbose_output     - The padded, wasteful version of the text
  efficient_output   - The compressed, token-efficient equivalent
  technique          - Compression strategy used
  subcategory        - Specific variant of the technique
  difficulty         - Tier 1 (easiest) -> Tier 8 (hardest)
  notes              - Reserved for future metadata

---

## Difficulty tiers

Tier 1 — remove_filler_words
  Strip padding phrases like "it is important to note that",
  "in order to", "due to the fact that", etc.

Tier 2 — use_shorter_synonyms
  Replace long phrases with single-word equivalents
  (utilize -> use, subsequently -> then, commence -> start ...).

Tier 3 — remove_redundant_context
  Delete information the reader already knows from the
  surrounding text or conversation history.

Tier 4 — use_structured_format
  Convert paragraph prose into key=value, lists, and
  machine-readable pipe-delimited strings.

Tier 5 — omit_trivial_steps
  Remove deductions the reader can derive themselves;
  jump straight to the conclusion.

Tier 6 — deduplicate_cross_fields
  Reference shared attributes instead of repeating them
  across multiple sentences or fields.

Tier 7 — domain_shorthand
  Use field-standard abbreviations (MI, PCI, DAPT, TTL, TAR)
  that practitioners understand but novices do not need spelled out.

Tier 8 — compress_nested_reasoning
  Collapse multi-step chains of logic (cause -> effect -> decision)
  into a single supported conclusion with the key constraints only.

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## Training usage

Load with pandas:

    import pandas as pd
    df = pd.read_csv("token_efficiency_corpus.csv")

Supervised compression task:

    X = df["verbose_output"]
    y = df["efficient_output"]

Classification task (identify compression technique):

    cls_y = df[["technique", "difficulty"]]

Fine-tuning an LLM with the pair as input -> target:

    # input   = "Compress this:\n{verbose_output}"
    # target  = efficient_output

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## Design notes

- Streaming-first generation — rows are flushed to disk continuously,
  keeping peak memory small.
- QUOTE_ALL CSV mode ensures commas and newlines inside fields
  never break parsing.
- Bidirectional training is possible with a reverse pass
  (efficient -> verbose) to teach both compression and expansion.
- Harder tiers get more rows because each example encodes a
  more nuanced compression insight.

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