Papers with GPT 3.5-Turbo

1 papers
Revisiting Zero-Shot Abstractive Summarization in the Era of Large Language Models from the Perspective of Position Bias (2024.naacl-short)

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Challenge: Position bias is a tendency of a model unfairly prioritizing information from certain parts of the input text over others, leading to undesirable behavior.
Approach: They propose to measure position bias in large language models for zero-shot summarization tasks by measuring position bias.
Outcome: The proposed model performance and position biases lead to new insights and discussion on zero-shot summarization tasks.

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