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Can we trust the evaluation on ChatGPT?
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ChatGPT, the first large language model (LLM) with mass adoption, has demonstrated remarkable performance in numerous natural language tasks. Despite its evident usefulness, evaluating ChatGPT's performance in diverse problem domains remains challenging due to the closed nature of the model and its continuous updates via Reinforcement Learning from Human Feedback (RLHF). We highlight the issue of data contamination in ChatGPT evaluations, with a case study of the task of stance detection. We discuss the challenge of preventing data contamination and ensuring fair model evaluation in the age of closed and continuously trained models.
Forward citations
Cited by 2 Pith papers
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Prompt engineering using order-of-addition experiments: An application to generating two-level fractional factorial designs
Order-of-addition designs and logistic pairwise-ordering models measure and optimize prompt-element order, lifting LLM success on 16-run fractional factorial design tasks from low teens or mid-thirties to near 100%.
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Strategic Prompting for Conversational Tasks: A Comparative Analysis of Large Language Models Across Diverse Conversational Tasks
No single open-source LLM among Llama, OPT, Falcon, Alpaca, and MPT performs best across reservation, empathy, counseling, persuasion, and negotiation tasks.
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