assessment-bench 0.2.0

Benchmark assessment approaches: pure-LLM marking vs the family's signal-based observations, with repeated runs and agreement statistics.

assessment-bench

Part of the lens family.

Benchmark assessment approaches. Run one cohort through competing assessment arms — pure-LLM marking (the baseline) and the family's signal-based observations (assessment-lens) — with repeated runs, consistency statistics, and agreement against human marks. The bench measures; it never marks.

assessment-bench is a bench (a measurement product), not an -analyser and not a marking tool. It exists to answer research questions like: how consistent is LLM marking across repeated runs and providers? and which deterministic signals actually track human judgement?

What it does

experiment.yaml (rubric + cohort + arms)
  ├─ llm arm(s)    : submission + rubric → provider → score   × repetitions
  ├─ signals arm   : assessment-lens → evidence values        (deterministic, once)
  └─ human marks   : optional ground-truth CSV
        ↓
result.json + runs.csv + signals.csv + agreement.csv
  • per-submission consistency: mean / median / std-dev / CV / reliability
  • agreement: Pearson & Spearman of every arm mean and every numeric signal
    against the human marks

Install

# from source (family layout)
uv venv && source .venv/bin/activate
uv pip install -e ".[dev]"

# the signals arm needs the analyser stack (bundle-analyser CLI on PATH):
uv pip install -e ".[analysers]"

# LLM arms (Anthropic, OpenAI, Ollama, OpenRouter):
uv pip install -e ".[llm]"      # + export ANTHROPIC_API_KEY / OPENAI_API_KEY / ...

Quick start

assessment-bench init experiment.yaml   # commented example config
# edit: point at your rubric.yaml + submissions/, choose arms
assessment-bench run experiment.yaml -o out/

LLM arms specify provider and model per arm — comparing claude-haiku-4-5 vs gpt-4o-mini vs a local llama3.1 via Ollama is just three arms in one config.

Relationship to the family

• Analysers generate deterministic signals (assessment-agnostic).
• assessment-lens maps signals to a rubric as observations — never scores.
• assessment-bench measures both approaches against human judgement. The LLM arm produces scores because that is the approach under test; the bench treats them as data points, not grades for students.

Status

v0.1 scaffold. Working today:

• ✅ Experiment config (YAML) → cohort discovery → arms → structured results
• ✅ LLM arm: multi-provider (anthropic / openai / ollama / openrouter), repeated runs, strict SCORE: x/y extraction with scaled fallback
• ✅ Signals arm: one assessment-lens pass; raw evidence values consumed (not the presence-based coverage)
• ✅ Consistency stats (ported from the original Rust prototype) + Pearson/Spearman agreement vs human marks
• 📋 Hybrid arm (LLM marking with analyser signals in context) — next
• 📋 HTTP service + desktop shell for non-technical researchers — planned

Development

pytest -v

License

MIT — see LICENSE.