Gugu8/Token-Efficiency

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.

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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

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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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