@inproceedings{liu-etal-2023-system,
title = "System Report for {CCL}23-Eval Task 9: {HUST}1037 Explore Proper Prompt Strategy for {LLM} in {MRC} Task",
author = "Liu, Xiao and
Yu, Junfeng and
He, Yibo and
Zhang, Lujun and
Wei, Kaiyichen and
Sun, Hongbo and
Tu, Gang",
editor = "Sun, Maosong and
Qin, Bing and
Qiu, Xipeng and
Jiang, Jing and
Han, Xianpei",
booktitle = "Proceedings of the 22nd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)",
month = aug,
year = "2023",
address = "Harbin, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2023.ccl-3.34",
pages = "310--319",
abstract = "{``}Our research paper delves into the Adversarial Robustness Evaluation for Chinese Gaokao Read-ing Comprehension (GCRC advRobust). While Chinese reading comprehension tasks havegained significant attention in recent years, previous methods have not proven effective for thischallenging dataset. We focus on exploring how prompt engineering can impact a model{'}s read-ing comprehension ability. Through our experiments using ChatGLM, GPT3.5, and GPT4, wediscovered a correlation between prompt and LLM reading comprehension ability, and found thatprompt engineering improves the performance of each model. Our team submitted the results ofour system evaluation, which ranked first in three indexes and total scores. Keywords{---} LLM, Prompt, Chinese Reading Comprehension{''}",
language = "English",
}
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<abstract>“Our research paper delves into the Adversarial Robustness Evaluation for Chinese Gaokao Read-ing Comprehension (GCRC advRobust). While Chinese reading comprehension tasks havegained significant attention in recent years, previous methods have not proven effective for thischallenging dataset. We focus on exploring how prompt engineering can impact a model’s read-ing comprehension ability. Through our experiments using ChatGLM, GPT3.5, and GPT4, wediscovered a correlation between prompt and LLM reading comprehension ability, and found thatprompt engineering improves the performance of each model. Our team submitted the results ofour system evaluation, which ranked first in three indexes and total scores. Keywords— LLM, Prompt, Chinese Reading Comprehension”</abstract>
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%0 Conference Proceedings
%T System Report for CCL23-Eval Task 9: HUST1037 Explore Proper Prompt Strategy for LLM in MRC Task
%A Liu, Xiao
%A Yu, Junfeng
%A He, Yibo
%A Zhang, Lujun
%A Wei, Kaiyichen
%A Sun, Hongbo
%A Tu, Gang
%Y Sun, Maosong
%Y Qin, Bing
%Y Qiu, Xipeng
%Y Jiang, Jing
%Y Han, Xianpei
%S Proceedings of the 22nd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)
%D 2023
%8 August
%I Chinese Information Processing Society of China
%C Harbin, China
%G English
%F liu-etal-2023-system
%X “Our research paper delves into the Adversarial Robustness Evaluation for Chinese Gaokao Read-ing Comprehension (GCRC advRobust). While Chinese reading comprehension tasks havegained significant attention in recent years, previous methods have not proven effective for thischallenging dataset. We focus on exploring how prompt engineering can impact a model’s read-ing comprehension ability. Through our experiments using ChatGLM, GPT3.5, and GPT4, wediscovered a correlation between prompt and LLM reading comprehension ability, and found thatprompt engineering improves the performance of each model. Our team submitted the results ofour system evaluation, which ranked first in three indexes and total scores. Keywords— LLM, Prompt, Chinese Reading Comprehension”
%U https://aclanthology.org/2023.ccl-3.34
%P 310-319
Markdown (Informal)
[System Report for CCL23-Eval Task 9: HUST1037 Explore Proper Prompt Strategy for LLM in MRC Task](https://aclanthology.org/2023.ccl-3.34) (Liu et al., CCL 2023)
ACL