llmscorecard 0.0.2

Regression-based LLM benchmark ratings + publication-ready scorecards.

LLMScorecard

Regression-based LLM benchmark ratings + publication-ready scorecards.

Title and one-line promise

LLMScorecard turns raw LLM evaluation results into defensible ratings (with uncertainty) and an offline HTML scorecard report.

Overview

LLMScorecard is not an eval runner. It does not prompt models, execute tasks, or manage datasets.

It is the “score modeling + reporting” layer you put downstream of your existing evaluation harness to:

• validate inputs (schema-first)
• fit research-based score models (regression with controls/interactions)
• quantify uncertainty (bootstrap and/or cluster-robust)
• emit reproducible artifacts you can publish and archive

Outputs are designed for org adoption: deterministic seeds, embedded config, schema versions, offline HTML, no phone-home.

Typical artifacts:

• results.json + fitted_model.json
• ratings.csv (leaderboard + intervals)
• report.html (shareable, offline)

Features

• Single-command report generation: llmscorecard report results.csv --out report.html
• Schema-first: validate + summarize
• Research-based ratings: fixed effects + interactions (LLM, category, task)
• Uncertainty: bootstrap and/or cluster-robust
• Reproducibility: config embedded, seed, schema version
• Exports: ratings.csv, tables.csv, plots
• Extensible adapters (future): lm-eval-harness, lighteval

Install

Requires Python >= 3.9.

pip install llmscorecard

Optional extras (future-proof; may be thin in early versions):

pip install "llmscorecard[report]"
pip install "llmscorecard[stats]"

Quickstart (CLI)

Create examples/results.csv with the required columns:

llm,category,task,item_id,score
gpt-4o-mini,math,arithmetic,1,0.92
gpt-4o-mini,math,arithmetic,2,0.88
llama-3.1-8b-instruct,math,arithmetic,1,0.74
llama-3.1-8b-instruct,math,arithmetic,2,0.70
gpt-4o-mini,code,unit_tests,3,0.81
llama-3.1-8b-instruct,code,unit_tests,3,0.63

Validate:

llmscorecard validate examples/results.csv

Generate an offline HTML scorecard:

llmscorecard report examples/results.csv --out report.html

This produces report.html (and, depending on your config/version, sidecar artifacts like ratings.csv).

Quickstart (Python)

Target API (stable intent; may evolve early on):

from llmscorecard import Scorecard

sc = Scorecard.from_csv("examples/results.csv")

sc.fit(
		model="fe_interaction_task",
		ci="bootstrap",
		n_boot=400,
		seed=123,
)

sc.report("report.html")

Example output

llmscorecard report … emits a CI-friendly summary and writes the HTML report:

report:
	leaderboard (overall):
		1) gpt-4o-mini              0.000  [ -0.042,  0.041]
		2) llama-3.1-8b-instruct   -0.138  [ -0.191, -0.087]

	top categories (mean effect):
		math                        +0.021
		code                        -0.017

	model card:
		model: fe_interaction_task
		formula: score ~ C(llm) + C(category) + C(task) + C(llm):C(category)
		ci: bootstrap (by item_id), n=400, seed=123
		n_rows: 6
		n_items: 3
		schema_version: 0.1
		artifacts: results.json, fitted_model.json, ratings.csv, report.html

Results schema

LLMScorecard expects long-form results (one row per item × model, optionally with weights).

Required:

• llm (string)
• category (string)
• task (string)
• item_id (string|int)
• score (float; higher is better)

Optional:

• metric (string; if you store multiple metrics in one file)
• weight (float; defaults to 1.0)
• meta (JSON; per-row metadata)

Canonical JSON:

• results.json includes schema_version: "0.1" and normalized fields.
• llmscorecard validate is the gatekeeper (fail fast, actionable errors).

Rating models

Current models:

• fe_interaction_task (default): score ~ C(llm) + C(category) + C(task) + C(llm):C(category)
• fe_interaction: score ~ C(llm) + C(category) + C(llm):C(category)
• pairwise_bt (future/experimental): Bradley–Terry for A/B preferences

Uncertainty:

• Bootstrap by item_id (clustered resampling) for CIs and stability checks.
• Cluster-robust SEs clustered by item_id (when aligned with your modeling assumptions).

Configuration

Example scorecard.yaml:

metric: score
model: fe_interaction_task
ci:
	method: bootstrap
	n: 400
	seed: 123
	cluster: item_id
filters:
	llms: ["gpt-4o-mini", "llama-3.1-8b-instruct"]
	categories: ["math", "code"]

Usage:

llmscorecard report results.json --config scorecard.yaml --out report.html

CLI usage

llmscorecard validate <results.(csv|json)>
llmscorecard summarize <results.(csv|json)>
llmscorecard ingest <results.csv> --out results.json
llmscorecard report <results.(csv|json)> --out report.html [--config scorecard.yaml]

Exit codes:

• validate: 0 ok, 2 invalid input
• summarize: 0 ok, 2 invalid input
• ingest: 0 ok, 2 invalid input
• report: 0 ok (warnings print to stderr but do not fail CI by default)

Development

git clone https://github.com/<user>/llmscorecard
cd llmscorecard
pip install -e .
pip install -e ".[dev]"
pytest
ruff check .

Build:

python -m build

Roadmap

• v0.0.3 schema validation + summarize
• v0.0.4 HTML report
• v0.0.5 regression ratings
• v0.0.6 uncertainty + P(A>B)
• v0.0.7 results.json artifacts
• v0.0.8 config
• v0.0.9 exports
• v0.1.0 stable schema + docs
• Future: adapters (lm-eval-harness, lighteval), mixed effects, IRT, hosted leaderboard generator

Contributing

Small, reviewable PRs win. Please include tests where they fit.

pytest
ruff check .

If you’re changing schemas or rating models, include a minimal fixture and expected artifacts.

License

MIT. See LICENSE.