Datasets:

massaindustries
/

dataset-A-routing

license: cc-by-4.0
language:
  - en
tags:
  - routing
  - evaluation
  - llm-router
  - multi-domain
  - model-comparison
size_categories:
  - 1K<n<10K
configs:
  - config_name: results
    default: true
    data_files:
      - split: train
        path: data/results/train.jsonl.gz
  - config_name: verbose
    data_files:
      - split: train
        path: data/verbose/train.jsonl.gz
  - config_name: evals
    data_files:
      - split: train
        path: data/evals/train.jsonl.gz

Dataset A - Routing (3 modelli, verdict-level)

Totale query: 5504 | Gated (masked) query: 0

Dataset di valutazione per LLM routing systems su 6 capability. Ogni query è stata eseguita su 3 modelli (qwen3.5-9b, deepseek-v4-flash, kimi2.6) e giudicata con grader deterministici (math/coding/ifeval) o LLM judge panel 2-of-3 (planning_agentic) / single judge (creative_synthesis, world_knowledge).

Configs

from datasets import load_dataset

# Pivot verdict per query (default config, 3 colonne bool)
ds_results = load_dataset("massaindustries/dataset-A-routing", split="train")

# Full: raw responses, costs, latency, 3x3 judge per planning, grader_meta
ds_verbose = load_dataset("massaindustries/dataset-A-routing", name="verbose", split="train")

# Prompts + ground truth (no model outputs) — per replicare i test
ds_evals = load_dataset("massaindustries/dataset-A-routing", name="evals", split="train")

# Tutti i config hanno una riga `_schema_anchor` (query_id == '_schema_anchor') con valori
# dummy per fissare lo schema di datasets/PyArrow. Filtrala via:
#   ds = ds.filter(lambda r: r["query_id"] != "_schema_anchor")

Schema `results`

campo	tipo	note
`query_id`	string	`q_NNNNN`
`query`	string	`<masked>` se gated
`dimension`	string	1 di 6 capability
`evaluation_protocol_id`	string	protocollo grader
`source`	string	dataset originale
`gated`	bool	dataset proprietary (query mascherata)
`qwen_correct`	bool/null	verdict primario qwen3.5-9b
`ds4_correct`	bool/null	verdict primario deepseek-v4-flash
`kimi_correct`	bool/null	verdict primario kimi2.6

*_correct è null quando il judge ha astensione (~1.4% del totale).

Schema `verbose`

Tutti i campi di results + per ogni modello m in {qwen, ds4, kimi}:

{m}_response (string, raw output)
{m}_thinking (string, raw thinking chain)
{m}_cost_usd (float, null per qwen)
{m}_latency_ms (int)
{m}_completion_tokens (int)
{m}_reasoning_tokens (int)
{m}_input_tokens (int)
{m}_finish_reason (string)
{m}_grader_meta (string, JSON serializzato)
{m}_model_id_real (string)
{m}_judge_gpt54mini (bool/null, solo planning ST)
{m}_judge_mistral (bool/null, solo planning ST)
{m}_judge_glm (bool/null, solo planning ST)

Verdict source per dimension

dimension	rows	verdict source
planning_agentic	1000	panel 2-of-3 (gpt-5.4-mini + mistral-small-2603 + glm-5-turbo) per ST; single judge per MT
math_reasoning	1000	deterministic (math_equiv, gsm8k_final_answer)
coding	1000	deterministic (unit_test_pass)
instruction_following	841	deterministic (ifeval_constraint_check)
world_knowledge	802	mix (deterministic mcq_letter per 102; LLM judge `llm_judge_factual` per 700)
creative_synthesis	696	LLM judge `rubric_judge` (gpt-5.4-mini)

Win-rate per modello

modello	correct	incorrect	abstention	accuracy
qwen	3478	1786	241	0.6607
ds4	4057	1220	228	0.7688
kimi	4130	784	591	0.8405

Coverage check

Ogni modello ha verdict per tutte le 5339 query:

modello	query con verdict	coverage
qwen	5339	97.0%
ds4	5339	97.0%
kimi	5339	97.0%

Reproducibility

Stesso pool di 5339 query del dataset base massaindustries/dataset-A-routing-eval
Inference deterministic (temperature=0.0)
Judge panel costanti, prompt template versionati nello skill llmevals
Per ripetere i test: scarica config evals, esegui inference, applica grader

License

CC-BY-4.0 (eccetto query originali dei dataset gated, che restano sotto le license dei rispettivi source).

Citation

Per i source originali del dataset_A, vedere lockfile.yaml in massaindustries/dataset-A-routing-eval.