Основные понятия
ChatGPT relies on word order for inference, challenging existing hypotheses.
Аннотация
The study explores the effects of word order on natural language processing, particularly focusing on ChatGPT. By conducting experiments with different datasets and tasks like order reconstruction and continuing generation, the research challenges the hypothesis that models do not rely on word order. Results show that disrupting word order significantly impacts certain datasets more than others, indicating the importance of word order in ChatGPT's performance. The study highlights the varying significance of word order in different contexts and tasks, emphasizing the need for diverse dataset analysis to understand its impact fully.
Статистика
The decline from best to worst results stands at (19%, 13%, 27%, 97%) for (RTP, CS, BF, Loop) datasets.
The average scores of deep disruptions are (0.51, 0.41), whereas those for superficial disruptions are (0.67, 0.63).
The disruption of word order leads to a (-13%, 0.1%, 35%, 26%) drop in performance for (RTP, CS, BF, Loop) datasets in continuing generation task.
Цитаты
"Existing works have studied the impacts of the order of words within natural text."
"In this paper, we revisit the aforementioned hypotheses by adding an order reconstruction perspective."
"Our contribution can be summarized: revisiting established hypotheses regarding the impact of word order from both reordering and generation perspectives."