- See Also
-
Links
- “Decomposition Enhances Reasoning via Self-Evaluation Guided Decoding”, Et Al 2023
- “Predicting Consumer Contracts [With GPT-3]”, 2023
- “Large Language Models As Fiduciaries: A Case Study Toward Robustly Communicating With Artificial Intelligence Through Legal Standards”, 2023
- “Can Large Language Models Reason about Medical Questions?”, Et Al 2022
- “Language Models (Mostly) Know What They Know”, Et Al 2022
- “Forecasting Future World Events With Neural Networks”, Et Al 2022
- “Beyond the Imitation Game: Quantifying and Extrapolating the Capabilities of Language Models”, Et Al 2022
- “Teaching Models to Express Their Uncertainty in Words”, Et Al 2022
- “Co-training Improves Prompt-based Learning for Large Language Models”, Et Al 2022
- “AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts”, Et Al 2021
- “Calibrate Before Use: Improving Few-Shot Performance of Language Models”, Et Al 2021
- “Reducing Conversational Agents’ Overconfidence through Linguistic Calibration”, Et Al 2020
- “GPT-3 Nonfiction”, Gwern 2020
- Miscellaneous
- Link Bibliography
See Also
Links
“Decomposition Enhances Reasoning via Self-Evaluation Guided Decoding”, Et Al 2023
“Decomposition Enhances Reasoning via Self-Evaluation Guided Decoding”
“Predicting Consumer Contracts [With GPT-3]”, 2023
“Large Language Models As Fiduciaries: A Case Study Toward Robustly Communicating With Artificial Intelligence Through Legal Standards”, 2023
“Can Large Language Models Reason about Medical Questions?”, Et Al 2022
“Language Models (Mostly) Know What They Know”, Et Al 2022
“Forecasting Future World Events With Neural Networks”, Et Al 2022
“Beyond the Imitation Game: Quantifying and Extrapolating the Capabilities of Language Models”, Et Al 2022
“Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models”
“Teaching Models to Express Their Uncertainty in Words”, Et Al 2022
“Co-training Improves Prompt-based Learning for Large Language Models”, Et Al 2022
“Co-training Improves Prompt-based Learning for Large Language Models”
“AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts”, Et Al 2021
“Calibrate Before Use: Improving Few-Shot Performance of Language Models”, Et Al 2021
“Calibrate Before Use: Improving Few-Shot Performance of Language Models”
“Reducing Conversational Agents’ Overconfidence through Linguistic Calibration”, Et Al 2020
“Reducing conversational agents’ overconfidence through linguistic calibration”
“GPT-3 Nonfiction”, Gwern 2020
Miscellaneous
Link Bibliography
-
2022-kolt.pdf: “Predicting Consumer Contracts [With GPT-3]”, Noam Kolt -
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4335945: “Large Language Models As Fiduciaries: A Case Study Toward Robustly Communicating With Artificial Intelligence Through Legal Standards”, John Nay -
https://arxiv.org/abs/2207.08143: “Can Large Language Models Reason about Medical Questions?”, Valentin Liévin, Christoffer Egeberg Hother, Ole Winther -
https://arxiv.org/abs/2207.05221#anthropic: “Language Models (Mostly) Know What They Know”, -
https://arxiv.org/abs/2206.15474: “Forecasting Future World Events With Neural Networks”, -
gpt-3-nonfiction: “GPT-3 Nonfiction”, gwern