Tarefa qrdata_socsci.391
qrdata_socsciagentT2publicépoca 1, tentativa 1Execução: 20261015T170010Z-suite-qrdata_socsci-scripted-7912b3Veredito
Família do método (das chamadas de ferramentas): ols (regression)
Pergunta feita ao analista
Fit a linear regression model using the Democratic margin in the two-party vote share as the response variable and the perceived competence for Democratic candidates as the predictor. What is the estimated slope of the model? Please round to the nearest thousandth.
Estimativa (infer.ols): coef.d.comp = 0,6604. Caminho de referência inferido à mão.
Valores esperados e de onde vêm
qrdata.gold.391
- 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 391 (0-based), field 'answer'
- Trecho
item 391 answer 0.660
- Valor como publicado
0.660- Valor
- 0,66
0.66 - Conferido por
- arquivo do benchmark
- 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:
[0.6402,0.6798000000000001]· interval · [0.6402, 0.6798000000000001] ()
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 0.66 in (0.6402, 0.6798)? | obrigatória | passou | [0.6402,0.6798000000000001] | 0,660.66 | interval · [0.6402, 0.6798000000000001] () | qrdata.gold.391 | call_736e4030 |
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
| Argumento | Referência | Agente | |
|---|---|---|---|
| cluster | — | — | não fixado pela referência |
| controls | — | [] | não fixado pela referência |
| dataset | "face_margin" | "face_margin" | igual |
| fe | — | [] | não fixado pela referência |
| intercept | — | true | não fixado pela referência |
| level | — | 0,950.95 | não fixado pela referência |
| se | — | "HC1" | não fixado pela referência |
| subset | — | — | não fixado pela referência |
| weights | — | — | não fixado pela referência |
| x | ["d.comp"] | ["d.comp"] | igual |
| y | "d_margin" | "d_margin" | igual |
Trajetória
Cada evento na ordem em que aconteceu: turnos do modelo, chamadas de ferramentas, guardas, transformações e pontuação.
- 0Início
Fit a linear regression model using the Democratic margin in the two-party vote share as the response variable and the perceived competence for Democratic candidates as the predictor. What is the estimated slope of the model? Please round to the nearest thousandth.
face(agent)Several psychologists have reported the intriguing result of an experiment showing that facial appearance predicts election outcomes better than chance. In their experiment, the researchers briefly showed student subjects the black-and-white head shots of two candidates from a US congressional election (winner and runner-up). The exposure of subjects to facial pictures lasted less than a second, and the subjects were then asked to evaluate the two candidates in terms of their perceived competence. The researchers used these competence measures to predict election outcomes. The key hypothesis is whether or not a within-a-second evaluation of facial appearance can predict election outcomes. The CSV data set, face.csv, contains the data from the experiment. Note that we include data only from subjects who did not know the candidates’ political parties, their policies, or even which candidate was the incumbent or challenger. They were simply making snap judgments about which candidate appeared more competent based on their facial expression alone. Variable Description congress: session of Congress year: year of the election state: state of the election winner: name of the winner loser: name of the runner-up w.party: party of the winner l.party: party of the loser d.votes: number of votes for the Democratic candidate r.votes: number of votes for the Republican candidate d.comp: competence measure for the Democratic candidate r.comp: competence measure for the Republican candidate
file3c3a97cca6fdd96f6856f7856b5f8bc786a31d1a9be78c9f610ef3070783e970· frame —face_margin(reference)file3c3a97cca6fdd96f6856f7856b5f8bc786a31d1a9be78c9f610ef3070783e970· frame —derive{"args":{"left":"d.votes","right":"r.votes"},"name":"d_minus_r","op":"diff"}derive{"args":{"columns":["d.votes","r.votes"]},"name":"two_party","op":"sum"}derive{"args":{"left":"d_minus_r","right":"two_party"},"name":"d_margin","op":"ratio"}
- 1Transformação
face_margin·derive{"args":{"left":"d.votes","right":"r.votes"},"name":"d_minus_r","op":"diff"}119 → 119 linhasFrame antes → depois:34b60d8b2348→d30e42c7976f - 2Transformação
face_margin·derive{"args":{"columns":["d.votes","r.votes"]},"name":"two_party","op":"sum"}119 → 119 linhasFrame antes → depois:d30e42c7976f→122bcc68db7b - 3Transformação
face_margin·derive{"args":{"left":"d_minus_r","right":"two_party"},"name":"d_margin","op":"ratio"}119 → 119 linhasFrame antes → depois:122bcc68db7b→4e22178635b2 - 4Turno do modelo
call_0f86b602referenceFim:tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cacheTexto visívelOLS of the Democratic two-party margin on the Democratic candidate's perceived competence.
Chamadas pedidasinfer.ols{ "dataset": "face_margin", "x": [ "d.comp" ], "y": "d_margin" }
- 5Ferramenta
infer.olsv0.1.0call_736e4030decidido pelo modeloreexecutávelMotivo: OLS of the Democratic two-party margin on the Democratic candidate's perceived competence.
Argumentos
{ "cluster": null, "controls": [], "dataset": "face_margin", "fe": [], "intercept": true, "level": 0.95, "se": "HC1", "subset": null, "weights": null, "x": [ "d.comp" ], "y": "d_margin" }Figuras call_736e4030 Chave Valor Valor bruto Erro-padrão IC coef.d.comp Coefficient on d.comp in the regression of d_margin0,660381 0.66038145677658420,125175 0.125175145682386940,412479 – 0,908284 Diagnósticosn_obs= 119119k= 22r2= 0,1872940.18729363431996082df_resid= 117117se_type="HC1"n_clusters= —
Tabelas (1)
{ "coefficients": [ { "ci_high": -0.17645418569832133, "ci_low": -0.4479975648598276, "coef": -0.31222587527907447, "p": 0.00001297028928131714, "se": 0.06855606261484216, "t": -4.554314576570776, "term": "Intercept" }, { "ci_high": 0.9082842734581125, "ci_low": 0.41247864009505597, "coef": 0.6603814567765842, "p": 6.152843299974919e-7, "se": 0.12517514568238694, "t": 5.2756595822321035, "term": "d.comp" } ] }- Digests dos dados
face_margin4e22178635b245e6c5ef97a118af1ff85fd434bfb578c46c3be31387e7657ee8- sha256 do resultado
36796479215316d5e697dcfbc84d5c50fc30704a437a9d72ebfb943a313f1b60
- 6Turno do modelo
call_7cde8b0freferenceFim:tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cacheChamadas pedidassubmit_answer(meta-ferramenta){ "answer": "Estimativa (infer.ols): coef.d.comp = 0,6604. Caminho de referência inferido à mão.", "caveats": [], "figure_refs": [ "call_736e4030:coef.d.comp" ] }
- 7Guardaresposta aceita
- Números encontrados
- 0,6604
- Sem lastro
- —
- Não citados
- —
1 nova(s) tentativa(s) restante(s) - 8Resposta
Estimativa (infer.ols): coef.d.comp = 0,6604. Caminho de referência inferido à mão.
Figuras citadas:call_736e4030:coef.d.comp· Parada:answeredFigura da resposta:call_736e4030:coef.d.comp= 0,6603810.6603814567765842(se 0,1251750.12517514568238694) - 9Pontuação#0resposta com lastropassouobrigatória
every number backed - 10Pontuação#1figura da resposta próximapassouobrigatória
0.66 in (0.6402, 0.6798)?
Épocas × verificações
| # | 0 | 1 |
|---|---|---|
| Época 1 | passou | passou |
Tentativas
Cada tentativa é seu próprio arquivo e é publicada; só a última de cada época é pontuada.
- época 1, tentativa 1passoupontuada(exibindo)
Downloads desta tarefa
14237aea517c5743bbc24dc1254a0cbdbc6b0a387923f713062526d428d160bcO 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/2387ffccb26195063df534f6a6889564cb05443a4160e3ee7e676b77083f101b.json | 981 B | 2387ffccb26195063df534f6a6889564cb05443a4160e3ee7e676b77083f101b |
| attachments/393e07da80495e327d1f3b3c857050aac6782e11bec323bbeeabca30d2573dbc.json | 290 B | 393e07da80495e327d1f3b3c857050aac6782e11bec323bbeeabca30d2573dbc |
| attachments/433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420.json | 2,4 KB | 433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420 |
| attachments/4ddbd217af32f1e3ddb3eba35bf5df7515b6ad517ba60e3084fbd9b0edabeba8.json | 2,4 KB | 4ddbd217af32f1e3ddb3eba35bf5df7515b6ad517ba60e3084fbd9b0edabeba8 |
| attachments/52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b.json | 64,0 KB | 52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b |
| attachments/679bacfa865670a2d2b9becae0b02eefed38a428ec9887d626ed4acecb0fdf30.json | 875 B | 679bacfa865670a2d2b9becae0b02eefed38a428ec9887d626ed4acecb0fdf30 |
| attachments/9bfa3a55f98f9e975792853457ff5935e61b8673973d0c4ba26a352bdb14c766.json | 236 B | 9bfa3a55f98f9e975792853457ff5935e61b8673973d0c4ba26a352bdb14c766 |
| native/qrdata_socsci/qrdata_socsci.391.e1.a1.json | 458 B | 1c82050eab156ac768e6037a8cb45cb8f15d9edf8f1d8c57f95aee6a87d70d1f |
| sources/qrdata.eval_py.json | 1,3 KB | d610b61f390d29ce767e08ce850eab5cb0dc4d13c1edb62d8cf3b0e49a3a291a |
| sources/qrdata.gold.391.json | 1,1 KB | d568650bf8afed9d45d9da9c33d8171e5da1b1fbae7861f5c2e9f4cf2b0d8604 |
| specs/qrdata_socsci.391.json | 5,6 KB | 2d61da9c9f8d7618ace956eed3e0888aad9a523eebdd8b452a01f96f89e16ef8 |
| traces/qrdata_socsci.391.e1.a1.jsonl | 16,1 KB | 919a420964f88e837cfa6865696dcbe4d12f0725ce4f9e482649030066f16d8e |
| trajectories/qrdata_socsci.391.e1.a1.jsonl | 12,3 KB | 653a3e08e78a363f38cbf15633f62b1384470f090b05b3f1e60d42f060fa34d8 |