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What makes large language models (LLMs) impressive is also what makes them hard to evaluate: their diversity of uses.
Ensemble methods in machine learning
Dietterich, T. G · 2000
Earlier work this paper cites.
Interactive machine learning
Fails, J. A. and Olsen Jr, D. R · 2003
Earlier work this paper cites.
Why and why not explanations improve the intelligibility of context-aware intelligent systems
Lim, B. Y., Dey, A. K., and Avrahami, D · 2009
Earlier work this paper cites.
Power to the people: The role of humans in interactive machine learning
Amershi, S., Cakmak, M., Knox, W. B., and Kulesza, T · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
Earlier work this paper cites.
Algorithms for multi-armed bandit problems
Kuleshov, V. and Precup, D · 2014
Earlier work this paper cites.
XGBoost: A scalable tree boosting system
Chen, T. and Guestrin, C · 2016
Earlier work this paper cites.
Interactive machine learning for health informatics: When do we need the human-in-the-loop?
Holzinger, A · 2016
Earlier work this paper cites.
Rationalizing neural predictions
Lei, T., Barzilay, R., and Jaakkola, T · 2016
Earlier work this paper cites.
Trends and trajectories for explainable, accountable and intelligible systems: An HCI research agenda
Abdul, A., Vermeulen, J., Wang, D., Lim, B. Y., and Kankanhalli, M · 2018
Earlier work this paper cites.
Peeking inside the black-box: A survey on explainable artificial intelligence (XAI)
Adadi, A. and Berrada, M · 2018
Earlier work this paper cites.
Learning to explain: An information-theoretic perspective on model interpretation
Chen, J., Song, L., Wainwright, M., and Jordan, M · 2018
Earlier work this paper cites.
BERT: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
Earlier work this paper cites.
Explaining explanations: An overview of interpretability of machine learning
Gilpin, L. H., Bau, D., Yuan, B. Z., Bajwa, A., Specter, M., and Kagal, L · 2018
Earlier work this paper cites.
Prolific.ac—A subject pool for online experiments
Palan, S. and Schitter, C · 2018
Earlier work this paper cites.
Explanations as mechanisms for supporting algorithmic transparency
Rader, E., Cotter, K., and Cho, J · 2018
Earlier work this paper cites.
INVASE: Instance-wise variable selection using neural networks
Yoon, J., Jordon, J., and van der Schaar, M · 2018
Earlier work this paper cites.
Interpretable neural predictions with differentiable binary variables
Bastings, J., Aziz, W., and Titov, I · 2019
Earlier work this paper cites.
The principles and limits of algorithm-in-the-loop decision making
Green, B. and Chen, Y · 2019
Earlier work this paper cites.
On human predictions with explanations and predictions of machine learning models: A case study on deception detection
Lai, V. and Tan, C · 2019
Cited alongside, same era.
Ask not what AI can do, but what AI should do: Towards a framework of task delegability
Lubars, B. and Tan, C · 2019
Cited alongside, same era.
Discovering the sweet spot of human-computer configurations: A case study in information extraction
Mackeprang, M., Müller-Birn, C., and Stauss, M. T · 2019
Cited alongside, same era.
Huggingface’s transformers: State-of-the-art natural language processing
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., et al · 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
Cited alongside, same era.
Evaluating human-language model interaction
Lee, M., Srivastava, M., Hardy, A., Thickstun, J., Durmus, E., Paranjape, A., Gerard-Ursin, I., Li, X. L., Ladhak, F., Rong, F., et al · 2022
Later among the works it cites.
Challenging BIG-bench tasks and whether chain-of-thought can solve them
Suzgun, M., Scales, N., Schärli, N., Gehrmann, S., Tay, Y., Chung, H. W., Chowdhery, A., Le, Q. V., Chi, E. H., Zhou, D., et al · 2022
Later among the works it cites.
Interactive machine learning: A state of the art review
Wondimu, N. A., Buche, C., and Visser, U · 2022
Later among the works it cites.
AI chains: Transparent and controllable human-AI interaction by chaining large language model prompts
Wu, T., Terry, M., and Cai, C. J · 2022
Later among the works it cites.
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Artificial intelligence, values, and alignment
Gabriel, I · 2020
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Measuring massive multitask language understanding
Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D., and Steinhardt, J · 2020
Cited alongside, same era.
Learning to faithfully rationalize by construction
Jain, S., Wiegreffe, S., Pinter, Y., and Wallace, B. C · 2020
Cited alongside, same era.
The effect of natural distribution shift on question answering models
Miller, J., Krauth, K., Recht, B., and Schmidt, L · 2020
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Measuring robustness to natural distribution shifts in image classification
Taori, R., Dave, A., Shankar, V., Carlini, N., Recht, B., and Schmidt, L · 2020
Cited alongside, same era.
Effect of confidence and explanation on accuracy and trust calibration in AI-assisted decision making
Zhang, Y., Liao, Q. V., and Bellamy, R. K · 2020
Cited alongside, same era.
Does the whole exceed its parts? The effect of AI explanations on complementary team performance
Bansal, G., Wu, T., Zhou, J., Fok, R., Nushi, B., Kamar, E., Ribeiro, M. T., and Weld, D · 2021
Cited alongside, same era.
Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al · 2023
Later among the works it cites.
Generative AI at work
Brynjolfsson, E., Li, D., and Raymond, L. R · 2023
Later among the works it cites.
A survey on evaluation of large language models
Chang, Y., Wang, X., Wang, J., Wu, Y., Yang, L., Zhu, K., Chen, H., Yi, X., Wang, C., Wang, Y., et al · 2023
Later among the works it cites.
Navigating the jagged technological frontier: Field experimental evidence of the effects of AI on knowledge worker productivity and quality
Dell’Acqua, F., McFowland, E., Mollick, E. R., Lifshitz-Assaf, H., Kellogg, K., Rajendran, S., Krayer, L., Candelon, F., and Lakhani, K. R · 2023
Later among the works it cites.
Beavertails: Towards improved safety alignment of LLM via a human-preference dataset
Ji, J., Liu, M., Dai, J., Pan, X., Zhang, C., Bian, C., Sun, R., Wang, Y., and Yang, Y · 2023
Later among the works it cites.
Jiang, A. Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D. S., Casas, D. d. l., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., et al · 2023
Later among the works it cites.
Experimental evidence on the productivity effects of generative artificial intelligence
Noy, S. and Zhang, W · 2023
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Do users write more insecure code with AI assistants?
Perry, N., Srivastava, M., Kumar, D., and Boneh, D · 2023
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Hyena hierarchy: Towards larger convolutional language models
Poli, M., Massaroli, S., Nguyen, E., Fu, D. Y., Dao, T., Baccus, S., Bengio, Y., Ermon, S., and Ré, C · 2023
Later among the works it cites.
Large language models encode clinical knowledge
Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al · 2023
Later among the works it cites.
Getting aligned on representational alignment
Sucholutsky, I., Muttenthaler, L., Weller, A., Peng, A., Bobu, A., Kim, B., Love, B. C., Grant, E., Achterberg, J., Tenenbaum, J. B., et al · 2023
Later among the works it cites.
Alpaca: A strong, replicable instruction-following model
Taori, R., Gulrajani, I., Zhang, T., Dubois, Y., Li, X., Guestrin, C., Liang, P., and Hashimoto, T. B · 2023
Later among the works it cites.
Llama 2: Open foundation and fine-tuned chat models
Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al · 2023
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Aligning large language models with human: A survey
Wang, Y., Zhong, W., Li, L., Mi, F., Zeng, X., Huang, W., Shang, L., Jiang, X., and Liu, Q · 2023
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Fundamental limitations of alignment in large language models
Wolf, Y., Wies, N., Levine, Y., and Shashua, A · 2023
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