OpenAI / MODEL EVIDENCE

InstructGPT 175B

12 benchmarks · 66 observations · 10 sources · released 2022-01-27

Scores at a glance

12 benchmarks · large dot: InstructGPT 175B; small dots: closest models where reported

76.682.4

InstructGPT 175B · HellaSwag · Overall accuracy · % ↗

8 more reported results

78.9% · InstructGPT 175B · HellaSwag · Overall accuracy · % · Epoch AI · HellaSwag ↗

78.1% · InstructGPT 175B · HellaSwag · Overall accuracy · % · Epoch AI · HellaSwag ↗

79.3% · InstructGPT 175B · HellaSwag · Overall accuracy · % · Epoch AI · HellaSwag ↗

78.9% · InstructGPT 175B · HellaSwag · Overall accuracy · % · Epoch AI · HellaSwag ↗

78.1% · InstructGPT 175B · HellaSwag · Overall accuracy · % · Epoch AI · HellaSwag ↗

79.3% · InstructGPT 175B · HellaSwag · Overall accuracy · % · Epoch AI · HellaSwag ↗

78.9% · InstructGPT 175B · HellaSwag · Overall accuracy · % · Epoch AI · HellaSwag ↗

78.1% · InstructGPT 175B · HellaSwag · Overall accuracy · % · Epoch AI · HellaSwag ↗

53.465.4

InstructGPT 175B · OpenBookQA · Accuracy · % ↗

6 more reported results

58.8% · InstructGPT 175B · OpenBookQA · Accuracy · % · Epoch AI · OpenBookQA ↗

57.6% · InstructGPT 175B · OpenBookQA · Accuracy · % · Epoch AI · OpenBookQA ↗

57.6% · InstructGPT 175B · OpenBookQA · Accuracy · % · Epoch AI · OpenBookQA ↗

65.4% · InstructGPT 175B · OpenBookQA · Accuracy · % · Epoch AI · OpenBookQA ↗

58.8% · InstructGPT 175B · OpenBookQA · Accuracy · % · Epoch AI · OpenBookQA ↗

57.6% · InstructGPT 175B · OpenBookQA · Accuracy · % · Epoch AI · OpenBookQA ↗

63.782.3

InstructGPT 175B · PIQA · Score · % ↗

7 more reported results

80.5% · InstructGPT 175B · PIQA · Score · % · Epoch AI · PIQA ↗

80.4% · InstructGPT 175B · PIQA · Score · % · Epoch AI · PIQA ↗

82.3% · InstructGPT 175B · PIQA · Score · % · Epoch AI · PIQA ↗

81% · InstructGPT 175B · PIQA · Score · % · Epoch AI · PIQA ↗

80.5% · InstructGPT 175B · PIQA · Score · % · Epoch AI · PIQA ↗

81% · InstructGPT 175B · PIQA · Score · % · Epoch AI · PIQA ↗

81% · InstructGPT 175B · PIQA · Score · % · Epoch AI · PIQA ↗

64.371.2

InstructGPT 175B · TriviaQA · EM · % ↗

8 more reported results

68% · InstructGPT 175B · TriviaQA · EM · % · Epoch AI · TriviaQA ↗

64.3% · InstructGPT 175B · TriviaQA · EM · % · Epoch AI · TriviaQA ↗

71.2% · InstructGPT 175B · TriviaQA · EM · % · Epoch AI · TriviaQA ↗

68% · InstructGPT 175B · TriviaQA · EM · % · Epoch AI · TriviaQA ↗

64.3% · InstructGPT 175B · TriviaQA · EM · % · Epoch AI · TriviaQA ↗

71.2% · InstructGPT 175B · TriviaQA · EM · % · Epoch AI · TriviaQA ↗

68% · InstructGPT 175B · TriviaQA · EM · % · Epoch AI · TriviaQA ↗

64.3% · InstructGPT 175B · TriviaQA · EM · % · Epoch AI · TriviaQA ↗

70.181.1

InstructGPT 175B · Winogrande · Accuracy · % ↗

12 more reported results

77.7% · InstructGPT 175B · Winogrande · Accuracy · % · Epoch AI · Winogrande ↗

73.2% · InstructGPT 175B · Winogrande · Accuracy · % · Epoch AI · Winogrande ↗

70.2% · InstructGPT 175B · Winogrande · Accuracy · % · Epoch AI · Winogrande ↗

77.7% · InstructGPT 175B · Winogrande · Accuracy · % · Epoch AI · Winogrande ↗

73.2% · InstructGPT 175B · Winogrande · Accuracy · % · Epoch AI · Winogrande ↗

70.2% · InstructGPT 175B · Winogrande · Accuracy · % · Epoch AI · Winogrande ↗

77.7% · InstructGPT 175B · Winogrande · Accuracy · % · Epoch AI · Winogrande ↗

73.2% · InstructGPT 175B · Winogrande · Accuracy · % · Epoch AI · Winogrande ↗

70.2% · InstructGPT 175B · Winogrande · Accuracy · % · Epoch AI · Winogrande ↗

77.7% · InstructGPT 175B · Winogrande · Accuracy · % · Epoch AI · Winogrande ↗

73.2% · InstructGPT 175B · Winogrande · Accuracy · % · Epoch AI · Winogrande ↗

70.2% · InstructGPT 175B · Winogrande · Accuracy · % · Epoch AI · Winogrande ↗

Closest models by score

Similar positions on shared benchmark leaderboards. Smaller gaps are closer.

There are not enough shared scored settings to identify close comparisons reliably.

Sources

Every plotted observation links to its evaluation. Reported configurations, benchmark versions and task subsets can differ; similarity describes shared scores, not training lift.

  1. Language Models are Few-Shot Learners ↗

    Brown, Tom B., Mann, Benjamin, Ryder, Nick, Subbiah, Melanie, Kaplan, Jared, Dhariwal, Prafulla et al. · 2020

    All 31 authors

    Brown, Tom B., Mann, Benjamin, Ryder, Nick, Subbiah, Melanie, Kaplan, Jared, Dhariwal, Prafulla, Neelakantan, Arvind, Shyam, Pranav, Sastry, Girish, Askell, Amanda, Agarwal, Sandhini, Herbert-Voss, Ariel, Krueger, Gretchen, Henighan, Tom, Child, Rewon, Ramesh, Aditya, Ziegler, Daniel M., Wu, Jeffrey, Winter, Clemens, Hesse, Christopher, Chen, Mark, Sigler, Eric, Litwin, Mateusz, Gray, Scott, Chess, Benjamin, Clark, Jack, Berner, Christopher, McCandlish, Sam, Radford, Alec, Sutskever, Ilya, Amodei, Dario

  2. GLaM: Efficient Scaling of Language Models with Mixture-of-Experts ↗

    Du, Nan, Huang, Yanping, Dai, Andrew M., Tong, Simon, Lepikhin, Dmitry, Xu, Yuanzhong et al. · 2021

    All 27 authors

    Du, Nan, Huang, Yanping, Dai, Andrew M., Tong, Simon, Lepikhin, Dmitry, Xu, Yuanzhong, Krikun, Maxim, Zhou, Yanqi, Yu, Adams Wei, Firat, Orhan, Zoph, Barret, Fedus, Liam, Bosma, Maarten, Zhou, Zongwei, Wang, Tao, Wang, Yu Emma, Webster, Kellie, Pellat, Marie, Robinson, Kevin, Meier-Hellstern, Kathleen, Duke, Toju, Dixon, Lucas, Zhang, Kun, Le, Quoc V, Wu, Yonghui, Chen, Zhifeng, Cui, Claire

  3. Scaling Language Models: Methods, Analysis & Insights from Training Gopher ↗

    Rae, Jack W., Borgeaud, Sebastian, Cai, Trevor, Millican, Katie, Hoffmann, Jordan, Song, Francis et al. · 2021

    All 80 authors

    Rae, Jack W., Borgeaud, Sebastian, Cai, Trevor, Millican, Katie, Hoffmann, Jordan, Song, Francis, Aslanides, John, Henderson, Sarah, Ring, Roman, Young, Susannah, Rutherford, Eliza, Hennigan, Tom, Menick, Jacob, Cassirer, Albin, Powell, Richard, Driessche, George van den, Hendricks, Lisa Anne, Rauh, Maribeth, Huang, Po-Sen, Glaese, Amelia, Welbl, Johannes, Dathathri, Sumanth, Huang, Saffron, Uesato, Jonathan, Mellor, John, Higgins, Irina, Creswell, Antonia, McAleese, Nat, Wu, Amy, Elsen, Erich, Jayakumar, Siddhant, Buchatskaya, Elena, Budden, David, Sutherland, Esme, Simonyan, Karen, Paganini, Michela, Sifre, Laurent, Martens, Lena, Li, Xiang Lorraine, Kuncoro, Adhiguna, Nematzadeh, Aida, Gribovskaya, Elena, Donato, Domenic, Lazaridou, Angeliki, Mensch, Arthur, Lespiau, Jean-Baptiste, Tsimpoukelli, Maria, Grigorev, Nikolai, Fritz, Doug, Sottiaux, Thibault, Pajarskas, Mantas, Pohlen, Toby, Gong, Zhitao, Toyama, Daniel, d'Autume, Cyprien de Masson, Li, Yujia, Terzi, Tayfun, Mikulik, Vladimir, Babuschkin, Igor, Clark, Aidan, Casas, Diego de Las, Guy, Aurelia, Jones, Chris, Bradbury, James, Johnson, Matthew, Hechtman, Blake, Weidinger, Laura, Gabriel, Iason, Isaac, William, Lockhart, Ed, Osindero, Simon, Rimell, Laura, Dyer, Chris, Vinyals, Oriol, Ayoub, Kareem, Stanway, Jeff, Bennett, Lorrayne, Hassabis, Demis, Kavukcuoglu, Koray, Irving, Geoffrey

  4. LLaMA: Open and Efficient Foundation Language Models ↗

    Touvron, Hugo, Lavril, Thibaut, Izacard, Gautier, Martinet, Xavier, Lachaux, Marie-Anne, Lacroix, Timothée et al. · 2023

    All 14 authors

    Touvron, Hugo, Lavril, Thibaut, Izacard, Gautier, Martinet, Xavier, Lachaux, Marie-Anne, Lacroix, Timothée, Rozière, Baptiste, Goyal, Naman, Hambro, Eric, Azhar, Faisal, Rodriguez, Aurelien, Joulin, Armand, Grave, Edouard, Lample, Guillaume

  5. The Falcon Series of Open Language Models ↗

    Almazrouei, Ebtesam, Alobeidli, Hamza, Alshamsi, Abdulaziz, Cappelli, Alessandro, Cojocaru, Ruxandra, Debbah, Mérouane et al. · 2023

    All 14 authors

    Almazrouei, Ebtesam, Alobeidli, Hamza, Alshamsi, Abdulaziz, Cappelli, Alessandro, Cojocaru, Ruxandra, Debbah, Mérouane, Goffinet, Étienne, Hesslow, Daniel, Launay, Julien, Malartic, Quentin, Mazzotta, Daniele, Noune, Badreddine, Pannier, Baptiste, Penedo, Guilherme

  6. Language Models are Few-Shot Learners ↗

    Brown, Tom B., Mann, Benjamin, Ryder, Nick, Subbiah, Melanie, Kaplan, Jared, Dhariwal, Prafulla et al. · 2020

    All 31 authors

    Brown, Tom B., Mann, Benjamin, Ryder, Nick, Subbiah, Melanie, Kaplan, Jared, Dhariwal, Prafulla, Neelakantan, Arvind, Shyam, Pranav, Sastry, Girish, Askell, Amanda, Agarwal, Sandhini, Herbert-Voss, Ariel, Krueger, Gretchen, Henighan, Tom, Child, Rewon, Ramesh, Aditya, Ziegler, Daniel M., Wu, Jeffrey, Winter, Clemens, Hesse, Christopher, Chen, Mark, Sigler, Eric, Litwin, Mateusz, Gray, Scott, Chess, Benjamin, Clark, Jack, Berner, Christopher, McCandlish, Sam, Radford, Alec, Sutskever, Ilya, Amodei, Dario

  7. CommonsenseQA 2.0: Exposing the Limits of AI through Gamification ↗

    Talmor, Alon, Yoran, Ori, Bras, Ronan Le, Bhagavatula, Chandra, Goldberg, Yoav, Choi, Yejin et al. · 2022

    All 7 authors

    Talmor, Alon, Yoran, Ori, Bras, Ronan Le, Bhagavatula, Chandra, Goldberg, Yoav, Choi, Yejin, Berant, Jonathan

  8. Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model ↗

    Smith, Shaden, Patwary, Mostofa, Norick, Brandon, LeGresley, Patrick, Rajbhandari, Samyam, Casper, Jared et al. · 2022

    All 20 authors

    Smith, Shaden, Patwary, Mostofa, Norick, Brandon, LeGresley, Patrick, Rajbhandari, Samyam, Casper, Jared, Liu, Zhun, Prabhumoye, Shrimai, Zerveas, George, Korthikanti, Vijay, Zhang, Elton, Child, Rewon, Aminabadi, Reza Yazdani, Bernauer, Julie, Song, Xia, Shoeybi, Mohammad, He, Yuxiong, Houston, Michael, Tiwary, Saurabh, Catanzaro, Bryan

  9. LLaMA: Open and Efficient Foundation Language Models ↗

    Touvron, Hugo, Lavril, Thibaut, Izacard, Gautier, Martinet, Xavier, Lachaux, Marie-Anne, Lacroix, Timothée et al. · 2023

    All 14 authors

    Touvron, Hugo, Lavril, Thibaut, Izacard, Gautier, Martinet, Xavier, Lachaux, Marie-Anne, Lacroix, Timothée, Rozière, Baptiste, Goyal, Naman, Hambro, Eric, Azhar, Faisal, Rodriguez, Aurelien, Joulin, Armand, Grave, Edouard, Lample, Guillaume

  10. ScienceQA — Science Question Answering leaderboard ↗

    ScienceQA Team

Names used in source tables

InstructGPT 175B