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Published in pre-print, 2023
…Using an essay editing task, we show that iterative self-refinement leads to deviation between the language model evaluator and human judgment, demonstrating that reward hacking can occur spontaneously in-context with the use of iterative self-refinement. In addition, we study conditions under which reward hacking occurs and observe two factors that affect reward hacking severity: model size and context sharing between the generator and the evaluator.
Recommended citation: Pan, J., He, H., Bowman, S., & Feng, S. (2023). Spontaneous Reward Hacking in Iterative Self-Refinement. [ArXiV](https://arxiv.org/abs/2407.04549)
Published in Findings of ACL, 2023
…we characterize two ways through which ICL leverages demonstrations. Task recognition (TR) captures the extent to which LLMs can recognize a task through demonstrations, even without ground-truth labels, and apply their pre-trained priors, whereas task learning (TL) is the ability to capture new input-label mappings unseen in pre-training. Using a wide range of classification datasets and three LLM families (GPT-3, LLaMA and OPT), we design controlled experiments to disentangle the roles of TR and TL in ICL…
Recommended citation: Pan, J., Gao, T., Chen, H., & Chen, D. (2023). What In-Context Learning "Learns" In-Context: Disentangling Task Recognition and Task Learning. Annual Meeting of the Association for Computational Linguistics. [ArXiV](https://arxiv.org/abs/2305.09731)
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#ZETAWIN #ZETAWIN #ZETAWIN
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Because I honestly think I’m a better reviewer than 90% of writers at IGN.
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Do you like hurting other people?
Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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