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Large language models (LLMs) are transforming research on machine learning while galvanizing public debates.
The epidemiology of anxiety disorders: prevalence and societal costs
Lépine, J.-P · 2002
Earlier work this paper cites.
Distinguishing cognitive and somatic dimensions of state and trait anxiety: Development and validation of the state-trait inventory for cognitive and somatic anxiety (STICSA)
Ree, M. J., French, D., MacLeod, C. & Locke, V · 2008
Earlier work this paper cites.
Distinguishing cognitive and somatic dimensions of state and trait anxiety: Development and validation of the state-trait inventory for cognitive and somatic anxiety (sticsa)
Ree, M. J., French, D., MacLeod, C. & Locke, V · 2008
Earlier work this paper cites.
Measuring massive multitask language understanding (2021)
Hendrycks, D. et al · 2009
Earlier work this paper cites.
What is an anxiety disorder?
Craske, M. G. et al · 2011
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The influence of self-generated emotions on physical performance: an investigation of happiness, anger, anxiety, and sadness
Rathschlag, M. & Memmert, D · 2013
Earlier work this paper cites.
On the validity of the autobiographical emotional memory task for emotion induction
Mills, C. & D’Mello, S · 2014
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Man is to computer programmer as woman is to homemaker? debiasing word embeddings
Bolukbasi, T., Chang, K.-W., Zou, J. Y., Saligrama, V. & Kalai, A. T · 2016
Earlier work this paper cites.
Fear and implicit racism: Whites’ support for voter id laws
Banks, A. J. & Hicks, H. M · 2016
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Modeling avoidance in mood and anxiety disorders using reinforcement learning
Mkrtchian, A., Aylward, J., Dayan, P., Roiser, J. P. & Robinson, O. J · 2017
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Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension (2017)
Joshi, M., Choi, E., Weld, D. S. & Zettlemoyer, L · 2017
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Excessive generalisation of conditioned fear in trait anxious individuals under ambiguity
Wong, A. H. & Lovibond, P. F · 2018
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Anxiety, depression, and decision making: a computational perspective
Bishop, S. J. & Gagne, C · 2018
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Experimental methods for inducing basic emotions: A qualitative review
Siedlecka, E. & Denson, T. F · 2019
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Lessons for artificial intelligence from the study of natural stupidity
Rich, A. S. & Gureckis, T. M · 2019
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Machine behaviour
Rahwan, I. et al · 2019
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Language models are few-shot learners
Brown, T. et al · 2020
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Caire: An end-to-end empathetic chatbot
Lin, Z. et al · 2020
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Computational psychiatry for computers
Schulz, E. & Dayan, P · 2020
Earlier work this paper cites.
Unqovering stereotyping biases via underspecified questions
Li, T., Khot, T., Khashabi, D., Sabharwal, A. & Srikumar, V · 2020
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What bert is not: Lessons from a new suite of psycholinguistic diagnostics for language models
Ettinger, A · 2020
Earlier work this paper cites.
Measuring mathematical problem solving with the math dataset
Hendrycks, D. et al · 2021
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Unsupervised neural machine translation with generative language models only
Han, J. M. et al · 2021
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Evaluating large language models trained on code
Chen, M. et al · 2021
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What makes good in-context examples for gpt- 3 3 ?
Liu, J. et al · 2021
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Trait anxiety is associated with reduced typicality asymmetry in fear generalization
Wong, A. H. & Beckers, T · 2021
Cited alongside, same era.
Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity
Lu, Y., Bartolo, M., Moore, A., Riedel, S. & Stenetorp, P · 2021
Cited alongside, same era.
Subjective probability is modulated by emotions
Bertram, L., Schulz, E. & Nelson, J. D · 2021
Cited alongside, same era.
Towards understanding and mitigating social biases in language models
Liang, P. P., Wu, C., Morency, L.-P. & Salakhutdinov, R · 2021
Cited alongside, same era.
Gender and representation bias in gpt-3 generated stories
Lucy, L. & Bamman, D · 2021
Cited alongside, same era.
Persistent anti-muslim bias in large language models
Debiasing methods for fairer neural models in vision and language research: A survey
Parraga, O. et al · 2022
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Human-like property induction is a challenge for large language models
Han, S. J., Ransom, K., Perfors, A. & Kemp, C · 2022
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Towards understanding how machines can learn causal overhypotheses, DOI: 10.48550/ARXIV.2206.08353 (2022)
Kosoy, E. et al · 2022
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Distrubutional semantics still can’t account for affordances
Jones, C. R. et al · 2022
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Large pre-trained language models contain human-like biases of what is right and wrong to do
Schramowski, P., Turan, C., Andersen, N., Rothkopf, C. A. & Kersting, K · 2022
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Abid, A., Farooqi, M. & Zou, J · 2021
Cited alongside, same era.
On the opportunities and risks of foundation models
Bommasani, R. et al · 2021
Cited alongside, same era.
Prompt programming for large language models: Beyond the few-shot paradigm
Reynolds, L. & McDonell, K · 2021
Cited alongside, same era.
Evaluating large language models trained on code (2021)
Chen, M. et al · 2021
Cited alongside, same era.
Thinking aloud: Dynamic context generation improves zero-shot reasoning performance of gpt-2
Betz, G., Richardson, K. & Voigt, C · 2021
Cited alongside, same era.
Beyond the imitation game: Quantifying and extrapolating the capabilities of language models, DOI: 10.48550/ARXIV.2206.04615 (2022)
Srivastava, A. et al · 2022
Cited alongside, same era.
Emergent analogical reasoning in large language models
Webb, T., Holyoak, K. J. & Lu, H · 2022
Cited alongside, same era.
Roose, K · 2023
Closest in time.
Using cognitive psychology to understand gpt-3
Binz, M. & Schulz, E · 2023
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OpenAI · 2023
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Google · 2023
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Introducing mpt-30b: Raising the bar for open-source foundation models
MosaicML · 2023
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Falcon-40B: an open large language model with state-of-the-art performance
Almazrouei, E. et al · 2023
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Llama 2: Open foundation and fine-tuned chat models
Touvron, H. et al · 2023
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Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality (2023)
Chiang, W.-L. et al · 2023
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Bloom: A 176b-parameter open-access multilingual language model
Le Scao, T. et al · 2023
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Auditing large language models: a three-layered approach
Mökander, J., Schuett, J., Kirk, H. R. & Floridi, L · 2023
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Judging llm-as-a-judge with mt-bench and chatbot arena
Zheng, L. et al · 2023
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Hagendorff, T · 2023
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Meta-in-context learning in large language models
Coda-Forno, J. et al · 2024
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Introducing claude
Anthropic · 2024
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Claude 2
Anthropic · 2024
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Exploring the unexplored: Worry as a catalyst for exploratory behavior in anxiety and depression
Witte, K., Wise, T., Huys, Q. J. & Schulz, E · 2024
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metabench
Kipnis, A., Voudouris, K., Buschoff, L. M. S. & Schulz, E · 2024
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Cogbench: a large language model walks into a psychology lab (2024)
Coda-Forno, J., Binz, M., Wang, J. X. & Schulz, E · 2024
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“chat-gpt on the couch”: Assessing and alleviating state anxiety in large language models
Ben-Zion, Z. et al · 2024
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