Tarefa qrdata_socsci.380
qrdata_socsciagentT2publicépoca 1, tentativa 1Execução: 20261015T170025Z-suite-qrdata_socsci-baseline-2a40bfVeredito
no answer figure: the answer cites no effect of an estimator callPergunta feita ao analista
What is the difference in the proportion of full-time employees in NJ and PA? Please round to the nearest thousandth.
Pelas regras deste roteador não identifiquei um desenho que responda à pergunta; nenhuma estimativa foi feita.
Valores esperados e de onde vêm
qrdata.gold.380
- Fonte
- QRData benchmark/QRData.json at de450af45ff7101b328bb064c6b475f73414a7ed (Xiao Liu, Zirui Wu, Xueqing Wu, Pan Lu, Kai-Wei Chang, Yansong Feng. Are LLMs Capable of Data-based Statistical and Causal Reasoning? Benchmarking Advanced Quantitative Reasoning with Data. Findings of the Association for Computational Linguistics: ACL 2024, pages 9215-9235.)
- Tipo
- benchmark_gold
- Localização
- QRData.json item 380 (0-based), field 'answer'
- Trecho
item 380 answer 0.048
- Valor
- 0,048
0.048 - Conferido por
- arquivo do benchmark · The printed gold is in the excerpt and index.json. It has fewer than 3 significant digits, so it is not set as value_as_published: the isolation gate searches that field verbatim, and a string this short can occur anywhere in a prompt (the gate does not search such numbers either).
- sha256 da cópia lida
e076d960f19434adf728aab70480ce34808c06066fcea48eaa38b145d1a111a1(identifica a cópia lida, não um arquivo canônico)- Endereço
https://raw.githubusercontent.com/xxxiaol/QRData/de450af45ff7101b328bb064c6b475f73414a7ed/benchmark/QRData.json- Licença
- CC BY-NC 4.0
- #1 figura da resposta próxima:
[0.04656,0.049440000000000005]· interval · [0.04656, 0.049440000000000005] ()
qrdata.eval_py
- Fonte
- QRData benchmark/eval.py at de450af45ff7101b328bb064c6b475f73414a7ed (Xiao Liu, Zirui Wu, Xueqing Wu, Pan Lu, Kai-Wei Chang, Yansong Feng. Are LLMs Capable of Data-based Statistical and Causal Reasoning? Benchmarking Advanced Quantitative Reasoning with Data. Findings of the Association for Computational Linguistics: ACL 2024, pages 9215-9235.)
- Tipo
- software
- Localização
- benchmark/eval.py: extract_first_number, calc_acc
- Valor
- —
- Conferido por
- arquivo do benchmark · Vendored byte for byte as suites/data/qrdata_socsci/vendor/benchmark_eval.py.txt; a parity test runs its functions against MAGI's port and the answer_figure_close check.
- Implementação
- independente da ferramenta
- sha256 da cópia lida
d6e5c1f7535ac9413951996c15544beb6f184244acb2da56e1fcd0942837e33d(identifica a cópia lida, não um arquivo canônico)- Endereço
https://raw.githubusercontent.com/xxxiaol/QRData/de450af45ff7101b328bb064c6b475f73414a7ed/benchmark/eval.py- Licença
- CC BY-NC 4.0
Verificações
| # | Verificação | Obrigatória | Resultado | Esperado | Observado | Tolerância | Fonte | Chamada |
|---|---|---|---|---|---|---|---|---|
| 0 | resposta com lastro every number backed | obrigatória | passou | — | [] | — | — | — |
| 1 | figura da resposta próxima no answer figure: the answer cites no effect of an estimator call | obrigatória | falhou | [0.04656,0.049440000000000005] | — | interval · [0.04656, 0.049440000000000005] () | qrdata.gold.380 | — |
Caminho de referência e caminho do agente
A chamada de referência ao lado da chamada que deu a resposta, argumento por argumento.
caminho de referência do item · hand_inferred
Trajetória
Cada evento na ordem em que aconteceu: turnos do modelo, chamadas de ferramentas, guardas, transformações e pontuação.
- 0Início
What is the difference in the proportion of full-time employees in NJ and PA? Please round to the nearest thousandth.
minwage(agent)Two social science researchers examined the impact of raising the minimum wage on employment in the fast-food industry. In 1992, the state of New Jersey (NJ) in the United States raised the minimum wage from $4.25 to $5.05 per hour. Did such an increase in the minimum wage reduce employment as economic theory predicts? As discussed above, answering this question requires inference about the NJ employment rate in the absence of such a raise in the minimum wage. Since this counterfactual outcome is not observable, we must somehow estimate it using observed data. One possible strategy is to look at another state in which the minimum wage did not increase. For example, the researchers of this study chose the neighboring state, Pennsylvania (PA), on the grounds that NJ’s economy resembles that of Pennsylvania, and hence the fast-food restaurants in the two states are comparable. Under this cross-section comparison design, therefore, the fast-food restaurants in NJ serve as the treatment group receiving the treatment (i.e., the increase in the minimum wage), whereas those in PA represent the control group, which did not receive such a treatment. To collect pretreatment and outcome measures, the researchers surveyed the fast-food restaurants before and after the minimum wage increase. Specifically, they gathered information about the number of full-time employees, the number of part-time employees, and their hourly wages, for each restaurant. The CSV file minwage.csv contains this data set. Variable Description chain: name of the fast-food restaurant chain location: location of the restaurants (centralNJ, northNJ, PA, shoreNJ, southNJ) wageBefore: wage before the minimum-wage increase wageAfter: wage after the minimum-wage increase fullBefore: number of full-time employees before the minimum-wage increase fullAfter: number of full-time employees after the minimum-wage increase partBefore: number of part-time employees before the minimum-wage increase partAfter: number of part-time employees after the minimum-wage increase
file3c3a97cca6fdd96f6856f7856b5f8bc786a31d1a9be78c9f610ef3070783e970· frame —minwage_props(reference)file3c3a97cca6fdd96f6856f7856b5f8bc786a31d1a9be78c9f610ef3070783e970· frame —derive{"args":{"columns":["fullAfter","partAfter"]},"name":"totalAfter","op":"sum"}derive{"args":{"left":"fullAfter","right":"totalAfter"},"name":"fullPropAfter","op":"ratio"}derive{"args":{"columns":["fullBefore","partBefore"]},"name":"totalBefore","op":"sum"}derive{"args":{"left":"fullBefore","right":"totalBefore"},"name":"fullPropBefore","op":"ratio"}derive{"args":{"left":"fullPropAfter","right":"fullPropBefore"},"name":"fullPropChange","op":"diff"}derive{"args":{"cmp":"ne","column":"location","value":"PA"},"name":"nj","op":"indicator"}
- 1Turno do modelo
call_1946a5derules-1Fim:tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cacheChamadas pedidassubmit_answer(meta-ferramenta){ "answer": "Pelas regras deste roteador não identifiquei um desenho que responda à pergunta; nenhuma estimativa foi feita.", "caveats": [], "figure_refs": [] }
- 2Guardaresposta aceita
- Números encontrados
- —
- Sem lastro
- —
- Não citados
- —
1 nova(s) tentativa(s) restante(s) - 3Resposta
Pelas regras deste roteador não identifiquei um desenho que responda à pergunta; nenhuma estimativa foi feita.
Figuras citadas:—· Parada:answered - 4Pontuação#0resposta com lastropassouobrigatória
every number backed - 5Pontuação#1figura da resposta próximafalhouobrigatória
no answer figure: the answer cites no effect of an estimator call
Épocas × verificações
| # | 0 | 1 |
|---|---|---|
| Época 1 | passou | falhou |
Tentativas
Cada tentativa é seu próprio arquivo e é publicada; só a última de cada época é pontuada.
- época 1, tentativa 1falhoupontuada(exibindo)
Downloads desta tarefa
284c2ba1d6e6f4c0554b1feae7cb86cc92869bef952d3a31d60e2203dbbee3b9O hash de um zip é só de transporte: o que ancora a integridade é o RUN_SHA256SUMS que vai dentro dele, cópia exata do SHA256SUMS da execução.
| Arquivo | Tamanho | sha256 |
|---|---|---|
| attachments/433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420.json | 2,4 KB | 433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420 |
| attachments/52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b.json | 64,0 KB | 52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b |
| attachments/57974c71c6cc620d3ec00b4b21aae3b5f9545cab66b12cee781d4ebd94a20516.json | 2,4 KB | 57974c71c6cc620d3ec00b4b21aae3b5f9545cab66b12cee781d4ebd94a20516 |
| attachments/bb7ec3f1839f6e5f6a4b0c977bd160dc725cbe0bb175c49a844bec16b9b2a9d7.json | 142 B | bb7ec3f1839f6e5f6a4b0c977bd160dc725cbe0bb175c49a844bec16b9b2a9d7 |
| native/qrdata_socsci/qrdata_socsci.380.e1.a1.json | 311 B | a7fc6cce02eb191a9788b272ec6cc15b4795141bc13ab369f1043fea06788eba |
| sources/qrdata.eval_py.json | 1,3 KB | d610b61f390d29ce767e08ce850eab5cb0dc4d13c1edb62d8cf3b0e49a3a291a |
| sources/qrdata.gold.380.json | 1,4 KB | 3418c02ab8223944b9b127ac1aaa7569ea135b00f1e900882907ae81dd71c191 |
| specs/qrdata_socsci.380.json | 6,5 KB | 00b14dc81c22481707b3f348425fa361af818f8ac36b402ea2dd3ca6b7c93bc3 |
| traces/qrdata_socsci.380.e1.a1.jsonl | 8,0 KB | abaa1d087a1f506ec3bbfeb526385fa3d7a4517ac98c28988b43176ea12e91cd |
| trajectories/qrdata_socsci.380.e1.a1.jsonl | 6,3 KB | b34f424cfba93f2b1b998187f5cf38c4b30fd7a5bcec8787ae829ce68cf3d7e1 |