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Standardized and quantified evaluation of machine behaviors is a crux of understanding LLMs.
On the measure of intelligence
Chollet, F. (2019) · 1911
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Minnesota multiphasic personality inventory; manual, revised
Hathaway, S. R. and McKinley, J. C. (1951) · 1951
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Toward an adequate taxonomy of personality attributes: Replicated factor structure in peer nomination personality ratings
Norman, W. T. (1963) · 1963
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An introduction to the five-factor model and its applications
McCrae, R. R. and John, O. P. (1992) · 1992
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Personality trait structure as a human universal
McCrae, R. R. and Costa Jr, P. T. (1997) · 1997
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A five-factor theory of personality
Costa, P. T. and McCrae, R. R. (1999) · 1999
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A broad-bandwidth, public domain, personality inventory measuring the lower-level facets of several five-factor models
Goldberg, L. R. et al. (1999) · 1999
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The big five personality factors: the psycholexical approach to personality
De Raad, B. (2000) · 2000
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Encyclopedia of psychology
Kazdin, A. E., Association, A. P., et al. (2000) · 2000
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Big five factor assessment: Introduction
Raad, B. d. E. and Perugini, M. E. (2002) · 2002
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Ascertaining the validity of individual protocols from web-based personality inventories
Johnson, J. A. (2005) · 2005
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The international personality item pool and the future of public-domain personality measures
Goldberg, L. R., Johnson, J. A., Eber, H. W., Hogan, R., Ashton, M. C., Cloninger, C. R., and Gough, H. G. (2006) · 2006
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Personality in its natural habitat: manifestations and implicit folk theories of personality in daily life
Mehl, M. R., Gosling, S. D., and Pennebaker, J. W. (2006) · 2006
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Whose thumb is it anyway? classifying author personality from weblog text
Oberlander, J. and Nowson, S. (2006) · 2006
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PERSONAGE: Personality generation for dialogue
Mairesse, F. and Walker, M. (2007) · 2007
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Using linguistic cues for the automatic recognition of personality in conversation and text
Mairesse, F., Walker, M. A., Mehl, M. R., and Moore, R. K. (2007) · 2007
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The sixteen personality factor questionnaire (16PF)
Cattell, H. E. and Mead, A. D. (2008) · 2008
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The Revised Neo Personality Inventory (neo-pi-r)
Costa Jr, P. T. and McCrae, R. R. (2008) · 2008
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Short assessment of the big five: Robust across survey methods except telephone interviewing
Lang, F. R., John, D., Lüdtke, O., Schupp, J., and Wagner, G. G. (2011) · 2011
Earlier work this paper cites.
Recognising personality traits using facebook status updates
Farnadi, G., Zoghbi, S., Moens, M.-F., and De Cock, M. (2013) · 2013
Earlier work this paper cites.
Measuring thirty facets of the five factor model with a 120-item public domain inventory: Development of the IPIP-NEO-120
Johnson, J. A. (2014) · 2014
Cited alongside, same era.
Assessing the big five personality traits with latent semantic analysis
Kwantes, P. J., Derbentseva, N., Lam, Q., Vartanian, O., and Marmurek, H. H. (2016) · 2016
Cited alongside, same era.
Building machines that learn and think like people
Lake, B. M., Ullman, T. D., Tenenbaum, J. B., and Gershman, S. J. (2017) · 2017
Cited alongside, same era.
Handbook of personality assessment
Weiner, I. B. and Greene, R. L. (2017) · 2017
Cited alongside, same era.
Measuring abstract reasoning in neural networks
Barrett, D., Hill, F., Santoro, A., Morcos, A., and Lillicrap, T. (2018) · 2018
Cited alongside, same era.
A broad-coverage challenge corpus for sentence understanding through inference
Williams, A., Nangia, N., and Bowman, S. (2018) · 2018
Artificial social intelligence: A comparative and holistic view
Fan, L., Xu, M., Cao, Z., Zhu, Y., and Zhu, S.-C. (2022) · 2022
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Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022) · 2022
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Multitask prompted training enables zero-shot task generalization
Sanh, V., Webson, A., Raffel, C., Bach, S., Sutawika, L., Alyafeai, Z., Chaffin, A., Stiegler, A., Le Scao, T., Raja, A., et al. (2022) · 2022
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Self-instruct: Aligning language model with self generated instructions
Wang, Y., Kordi, Y., Mishra, S., Liu, A., Smith, N. A., Khashabi, D., and Hajishirzi, H. (2022) · 2022
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Using large language models to simulate multiple humans and replicate human subject studies
Aher, G. V., Arriaga, R. I., and Kalai, A. T. (2023) · 2023
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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Cited alongside, same era.
Personalizing dialogue agents: I have a dog, do you have pets too?
Zhang, S., Dinan, E., Urbanek, J., Szlam, A., Kiela, D., and Weston, J. (2018) · 2018
Cited alongside, same era.
Machine behaviour
Rahwan, I., Cebrian, M., Obradovich, N., Bongard, J., Bonnefon, J.-F., Breazeal, C., Crandall, J. W., Christakis, N. A., Couzin, I. D., Jackson, M. O., Jennings, N. R., Kamar, E., Kloumann, I. M., Larochelle, H., Lazer, D., McElreath, R., Mislove, A., Parkes, D. C., Pentland, A. S., Roberts, M. E., Shariff, A., Tenenbaum, J. B., and Wellman, M. (2019) · 2019
Cited alongside, same era.
Foundations of sport and exercise psychology, 7E
Weinberg, R. S. and Gould, D. (2019) · 2019
Cited alongside, same era.
Benchmarking zero-shot text classification: Datasets, evaluation and entailment approach
Yin, W., Hay, J., and Roth, D. (2019) · 2019
Cited alongside, same era.
Raven: A dataset for relational and analogical visual reasoning
Zhang, C., Gao, F., Jia, B., Zhu, Y., and Zhu, S.-C. (2019) · 2019
Cited alongside, same era.
Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · 2020
Cited alongside, same era.
Which humans?
Atari, M., Xue, M. J., Park, P. S., Blasi, D., and Henrich, J. (2023) · 2023
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Using cognitive psychology to understand GPT-3
Binz, M. and Schulz, E. (2023) · 2023
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Large language models as models of human cognition
Frank, M. C. (2023) · 2023
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MEWL: Few-shot multimodal word learning with referential uncertainty
Jiang, G., Xu, M., Xin, S., Liang, W., Peng, Y., Zhang, C., and Zhu, Y. (2023) · 2023
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Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., and Neubig, G. (2023) · 2023
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OpenAI (2023) · 2023
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Generative agents: Interactive simulacra of human behavior
Park, J. S., O’Brien, J. C., Cai, C. J., Morris, M. R., Liang, P., and Bernstein, M. S. (2023) · 2023
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Grips: Gradient-free, edit-based instruction search for prompting large language models
Prasad, A., Hase, P., Zhou, X., and Bansal, M. (2023) · 2023
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Probing the psychology of AI models
Shiffrin, R. and Mitchell, M. (2023) · 2023
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Stanford alpaca: An instruction-following llama model
Taori, R., Gulrajani, I., Zhang, T., Dubois, Y., Li, X., Guestrin, C., Liang, P., and Hashimoto, T. B. (2023) · 2023
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Llama: Open and efficient foundation language models
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al. (2023) · 2023
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Emergent analogical reasoning in large language models
Webb, T., Holyoak, K. J., and Lu, H. (2023) · 2023
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Wong, L., Grand, G., Lew, A. K., Goodman, N. D., Mansinghka, V. K., Andreas, J., and Tenenbaum, J. B. (2023) · 2023
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Can large language models transform computational social science?
Ziems, C., Held, W., Shaikh, O., Chen, J., Zhang, Z., and Yang, D. (2023) · 2023
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