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Fine-Tuning Human for LLM Projects

https://doi.org/10.2139/ssrn.4574477
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1145/3491101.3519665openalex-extracted
Expectation vs. Experience: Evaluating the Usability of Code Generation Tools Powered by Large Language Models
inferredcites →https://doi.org/10.1145/602382.602400openalex-extracted
Some challenges and grand challenges for computational intelligence
inferredcites →https://doi.org/10.31234/osf.io/vtknhopenalex-extracted
Rationalizing Constraints on the Capacity for Cognitive Control
inferredcites →https://doi.org/10.48550/arxiv.2302.00093openalex-extracted
Large Language Models Can Be Easily Distracted by Irrelevant Context
inferredcites →https://doi.org/10.48550/arxiv.2302.05206openalex-extracted
The Wisdom of Hindsight Makes Language Models Better Instruction Followers
inferredcites →https://doi.org/10.48550/arxiv.2307.09288openalex-extracted
Behavioral Safety and Context Retention of Large Language Models in a Longitudinal ICU Simulation under Offline Conditions
inferredcites →https://doi.org/10.48550/arxiv.2309.00267openalex-extracted
RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback
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