Datasets:

luigicfilho
/

LadderBench

language:
  - en
license: cc-by-4.0
task_categories:
  - question-answering
  - multiple-choice
task_ids:
  - multiple-choice-qa
size_categories:
  - n<1K
configs:
  - config_name: default
    data_files:
      - split: level_1_easy
        path: level1_easy.jsonl
      - split: level_2_basic
        path: level2_basic.jsonl
      - split: level_3_intermediate
        path: level3_intermediate.jsonl
      - split: level_4_hard
        path: level4_hard.jsonl
      - split: level_5_expert
        path: level5_expert.jsonl
tags:
  - benchmark
  - evaluation
  - reasoning
  - graded-difficulty
  - llm-evaluation
pretty_name: LadderBench

LadderBench

A graded capability ladder for evaluating (large) language models: 150 multiple-choice questions organized in 5 difficulty levels (30 each), strictly ordered from easy to expert. Because every level is scored separately, LadderBench shows where a model's abilities break down, not just an average score - two models with the same overall accuracy can have very different level profiles.

Motivation

Averaged benchmark scores hide useful structure: a model can score 70% by acing easy questions and guessing on hard ones, or by being uniformly mediocre. LadderBench's ladder design exposes that difference:

Level	Name	What it probes	Example
1	easy	basic facts, single-step arithmetic	"What is 7 + 8?"
2	basic	everyday knowledge, word problems, gentle traps	"Which month has at least 28 days?"
3	intermediate	reading comprehension, applied reasoning, classic traps	bat & ball ($0.05), lily pads (day 29)
4	hard	multi-step math, sequences, work rates, percentages	clock-angle 7.5°, chicken/rabbit legs
5	expert	competition math, logic puzzles	2^50 mod 7, trailing zeros of 100!, knights & knaves

Expected behavior of a healthy model: a roughly monotonic decline from level 1 to level 5. A model that scores better on level 5 than level 4 is a signal of noise (too few samples) or a poorly calibrated level - which is exactly what this design is meant to catch.

Data format

One JSON object per line (JSONL), identical schema in every file:

{
  "id": "l5_12",
  "level": 5,
  "category": "math",
  "question": "How many trailing zeros does 100! (100 factorial) have?",
  "choices": ["10", "21", "24", "25"],
  "answer": "C"
}

Fields:

id - stable unique identifier (l<level>_<nn>)
level - 1..5
category - arithmetic / knowledge / common-sense / reading / math / logic / sequence / probability / trick / reasoning / safety
question - the prompt shown to the model (passages are embedded in the text)
choices - exactly 4 answer options, presented to the model as (A)-(D)
answer - the gold letter, one of A/B/C/D

Splits

File	Rows	Level
`level1_easy.jsonl`	30	1 easy
`level2_basic.jsonl`	30	2 basic
`level3_intermediate.jsonl`	30	3 intermediate
`level4_hard.jsonl`	30	4 hard
`level5_expert.jsonl`	30	5 expert

Evaluation protocol

The recommended protocol (implemented in the standalone runner):

Present the question with options lettered (A)-(D); instruct the model to answer with the letter only.
Parse the selected letter from the response (robust to "(B)", "B.", etc.).
Score 1.0 for an exact letter match, else 0.0; report per-level accuracy.
Use temperature 0 (greedy) for reproducibility.

Important: the random-guessing baseline is 25% (4 options). With 30 questions per level, each question is worth ~3.3 percentage points - treat per-level gaps under ~10 points cautiously and report the guessing baseline alongside results.

Standalone runner

This folder ships run_ladderbench.py, a zero-dependency runner (Python 3.8+ standard library only) so anyone can evaluate any model on LadderBench without installing anything else. It works with any OpenAI-compatible chat endpoint:

# Ollama (default endpoint)
python run_ladderbench.py --model qwen2.5:3b-instruct-q4_K_M

# LM Studio
python run_ladderbench.py --model my-model --base-url http://127.0.0.1:1234/v1 --api-key lmstudio

# llama.cpp llama-server
python run_ladderbench.py --model any-name --base-url http://127.0.0.1:8080/v1

# OpenAI / other hosted APIs
python run_ladderbench.py --model gpt-4o --base-url https://api.openai.com/v1 --api-key sk-...

# Quick pass: only hard levels, 5 questions each
python run_ladderbench.py --model <model> --levels 4,5 --limit 5

What it does: letter-answer prompting over every question, robust letter parsing, per-level accuracy table, and a JSON report (results_<model>_<timestamp>.json) containing a full per-question audit trail (prediction, gold, raw output) for error analysis.

Protocol notes:

temperature 0 (greedy) by default for reproducibility;
prompt asks for the letter only; parsing tolerates "(B)", "B.", etc.;
report the 25% guessing baseline next to results;
the audit file lets you compute per-category scores too (see category).

Extending / contributing

Add rows to any level file following the schema above (keep ids unique), or create a new level file and register it in the loader. When adding questions: verify the gold answer independently, make distractors plausible, and avoid answer-letter position bias by distributing correct letters across A-D.

License

CC-BY-4.0. Questions are original works written for this benchmark; classic puzzle ideas (bat & ball, lily pads, knights & knaves) are folklore and are rephrased here.