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Tarefa qrdata_socsci.391

qrdata_socsciagentT2publicépoca 1, tentativa 1Execução: 20261015T170010Z-suite-qrdata_socsci-scripted-7912b3

Veredito

passouPassou em todas as verificações obrigatórias.

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.

Resposta

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
0,660.66
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

    Cada verificação, o que esperava, o que observou e a chamada que leu
    #VerificaçãoObrigatóriaResultadoEsperadoObservadoTolerânciaFonteChamada
    0resposta com lastro
    every number backed
    obrigatóriapassou—[]———
    1figura da resposta próxima
    0.66 in (0.6402, 0.6798)?
    obrigatóriapassou[0.6402,0.6798000000000001]0,660.66interval · [0.6402, 0.6798000000000001] ()qrdata.gold.391call_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

    Referência: infer.olsChamada do agente: infer.ols (call_736e4030)igual
    Argumentos da referência e do agente
    ArgumentoReferênciaAgente
    cluster——não fixado pela referência
    controls—[]não fixado pela referência
    dataset"face_margin""face_margin"igual
    fe—[]não fixado pela referência
    intercept—truenão fixado pela referência
    level—0,950.95não 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.

    1. 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

      file 3c3a97cca6fdd96f6856f7856b5f8bc786a31d1a9be78c9f610ef3070783e970 · frame —
      face_margin (reference)
      file 3c3a97cca6fdd96f6856f7856b5f8bc786a31d1a9be78c9f610ef3070783e970 · frame —
      1. derive {"args":{"left":"d.votes","right":"r.votes"},"name":"d_minus_r","op":"diff"}
      2. derive {"args":{"columns":["d.votes","r.votes"]},"name":"two_party","op":"sum"}
      3. derive {"args":{"left":"d_minus_r","right":"two_party"},"name":"d_margin","op":"ratio"}
    2. 1Transformação
      face_margin · derive {"args":{"left":"d.votes","right":"r.votes"},"name":"d_minus_r","op":"diff"}
      119 → 119 linhas
      Frame antes → depois: 34b60d8b2348 → d30e42c7976f
    3. 2Transformação
      face_margin · derive {"args":{"columns":["d.votes","r.votes"]},"name":"two_party","op":"sum"}
      119 → 119 linhas
      Frame antes → depois: d30e42c7976f → 122bcc68db7b
    4. 3Transformação
      face_margin · derive {"args":{"left":"d_minus_r","right":"two_party"},"name":"d_margin","op":"ratio"}
      119 → 119 linhas
      Frame antes → depois: 122bcc68db7b → 4e22178635b2
    5. 4Turno do modelo
      call_0f86b602referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache
      Texto visível

      OLS of the Democratic two-party margin on the Democratic candidate's perceived competence.

      Chamadas pedidas
      • infer.ols
        {
          "dataset": "face_margin",
          "x": [
            "d.comp"
          ],
          "y": "d_margin"
        }
    6. 5Ferramenta
      infer.olsv0.1.0call_736e4030decidido pelo modeloreexecutável

      Motivo: 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
      ChaveValorValor brutoErro-padrãoIC
      coef.d.comp
      Coefficient on d.comp in the regression of d_margin
      0,660381 0.66038145677658420,1251750.125175145682386940,412479 – 0,908284
      Diagnósticos
      • n_obs = 119119
      • k = 22
      • r2 = 0,1872940.18729363431996082
      • df_resid = 117117
      • se_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_margin 4e22178635b245e6c5ef97a118af1ff85fd434bfb578c46c3be31387e7657ee8
      sha256 do resultado
      36796479215316d5e697dcfbc84d5c50fc30704a437a9d72ebfb943a313f1b60
    7. 6Turno do modelo
      call_7cde8b0freferenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache
      Chamadas pedidas
      • submit_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"
          ]
        }
    8. 7Guarda
      resposta aceita
      Números encontrados
      0,6604
      Sem lastro
      —
      Não citados
      —
      1 nova(s) tentativa(s) restante(s)
    9. 8Resposta

      Estimativa (infer.ols): coef.d.comp = 0,6604. Caminho de referência inferido à mão.

      Figuras citadas: call_736e4030:coef.d.comp · Parada: answered
      Figura da resposta: call_736e4030:coef.d.comp = 0,6603810.6603814567765842 (se 0,1251750.12517514568238694)
    10. 9Pontuação
      #0resposta com lastropassouobrigatóriaevery number backed
    11. 10Pontuação
      #1figura da resposta próximapassouobrigatória0.66 in (0.6402, 0.6798)?

    Épocas × verificações

    Resultado de cada verificação em cada época pontuada
    #01
    Época 1passoupassou

    Tentativas

    Cada tentativa é seu próprio arquivo e é publicada; só a última de cada época é pontuada.

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