Tarefa qrdata_socsci.409
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 proportion of voters whose races are black are correctly classified in their racial category? Race is considered correctly classified if the racial category with the greatest conditional probability P(race | surname, residence) is identical to the self-reported race. Please remove those voters who contain at least a missing value. Please round to the nearest hundredth.
Pelas regras deste roteador não identifiquei um desenho que responda à pergunta; nenhuma estimativa foi feita.
Valores esperados e de onde vêm
qrdata.gold.409
- 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 409 (0-based), field 'answer'
- Trecho
item 409 answer 0.63
- Valor
- 0,63
0.63 - 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:
[0.6111,0.6489]· interval · [0.6111, 0.6489] ()
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 | [0.6111,0.6489] | — | interval · [0.6111, 0.6489] () | qrdata.gold.409 | — |
Caminho de referência e caminho do agente
A chamada de referência ao lado da chamada que deu a resposta, argumento por argumento.
Nenhum caminho de referência registrado para esta tarefa.
Trajetória
Cada evento na ordem em que aconteceu: turnos do modelo, chamadas de ferramentas, guardas, transformações e pontuação.
- 0Início
What proportion of voters whose races are black are correctly classified in their racial category? Race is considered correctly classified if the racial category with the greatest conditional probability P(race | surname, residence) is identical to the self-reported race. Please remove those voters who contain at least a missing value. Please round to the nearest hundredth.
FLCensusVTD(agent)Data file FLCensusVTD.csv of this question; the question's data description is the description of dataset 'FLVoters'.
file3c3a97cca6fdd96f6856f7856b5f8bc786a31d1a9be78c9f610ef3070783e970· frame —FLVoters(agent)Here is a random sample of 10,000 registered voters from Florida contained in the CSV file FLVoters.csv. Variable Description surname: surname county: county ID of the voter’s residence VTD: voting district ID of the voter’s residence age: age gender: gender m = male and f = female race: self-reported race The US Census Bureau releases a list of common surnames with their frequency. For example, the most common surname was “Smith” with 2,376,206 occurrences, followed by “Johnson” and “Williams” with 1,857,160 and 1,534,042, respectively. This data set is quite comprehensive, including a total of more than 150,000 surnames that occurred at least 100 times. In addition, the census provides the relative frequencies of individual race within each surname, using a six-category self-reported race measure: non-Hispanic white, non-Hispanic black, non-Hispanic Asian and Pacific Islander, Hispanic origin, non-Hispanic American Indian and Alaskan Native, and non-Hispanic of two or more races. This census name list is contained in the CSV data file names.csv. Variable Description surname: surname count: number of individuals with a specific surname pctwhite: percentage of non-Hispanic whites among those who have a specific surname pctblack: percentage of non-Hispanic blacks among those who have a specific surname pctapi: percentage of non-Hispanic Asians and Pacific Islanders among those who have a specific surname pcthispanic: percentage of Hispanic origin among those who have a specific surname pctothers: percentage of the other racial groups among those who have a specific surname In the United States, voter files contain voters’ addresses. Using this information, our data set also provides the voting district where each voter lives. In addition, we will utilize the Florida census data, which contains the racial composition of each voting district. The census data set is in FLCensusVTD.csv. Variable Description county: county census ID of the voting district VTD: voting district census ID (only unique within the county) total.pop: total population of the voting district white: proportion of non-Hispanic whites in the voting district black: proportion of non-Hispanic blacks in the voting district api: proportion of non-Hispanic Asians and Pacific Islanders in the voting district hispanic: proportion of voters of Hispanic origin in the voting district others: proportion of the other racial groups in the voting district
file3c3a97cca6fdd96f6856f7856b5f8bc786a31d1a9be78c9f610ef3070783e970· frame —names(agent)Data file names.csv of this question; the question's data description is the description of dataset 'FLVoters'.
file3c3a97cca6fdd96f6856f7856b5f8bc786a31d1a9be78c9f610ef3070783e970· frame — - 1Turno do modelo
call_16ddc6berules-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
6c6f0a2b2b28c17afeb2b772c1a6d2ad36e5ce17ecb75a0c4fe5fe380a5ec4b8O 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/0bed69bc34d9fdc2dd42ce3d75d4a505a540e2bc2c445b96ac05b626de9e57ce.json | 3,6 KB | 0bed69bc34d9fdc2dd42ce3d75d4a505a540e2bc2c445b96ac05b626de9e57ce |
| attachments/433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420.json | 2,4 KB | 433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420 |
| attachments/52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b.json | 64,0 KB | 52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b |
| attachments/df18e70deee6f243a72c24b6b383a2d5c6c06560f7c0387dd226b40714ce2be0.json | 401 B | df18e70deee6f243a72c24b6b383a2d5c6c06560f7c0387dd226b40714ce2be0 |
| native/qrdata_socsci/qrdata_socsci.409.e1.a1.json | 310 B | 3df7270ba44a4ba6606b9acd2bb928a181e13f684389e6bd1b57b4433149ac20 |
| sources/qrdata.eval_py.json | 1,3 KB | d610b61f390d29ce767e08ce850eab5cb0dc4d13c1edb62d8cf3b0e49a3a291a |
| sources/qrdata.gold.409.json | 1,4 KB | 4f3dc0008c7d2bd6fe544374c1b679d6d77747a8d5fc1041e55760634d0c0c9c |
| specs/qrdata_socsci.409.json | 7,0 KB | 4d707db634068e371356d52556daba3115df5e2dcc2bfc1141decbfe0318a2f2 |
| traces/qrdata_socsci.409.e1.a1.jsonl | 9,4 KB | 2b1b8e967a629b1f321e7f5e85230818f7ff2dcd82b6d34ba3e906d7296bf0a3 |
| trajectories/qrdata_socsci.409.e1.a1.jsonl | 6,7 KB | e758070fbf86831f32fa36f68460c4ae031f5d85e89754f5c1bf5099d67446be |