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.
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
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.