@inproceedings{yang-etal-2023-failures,
title = "Failures Pave the Way: Enhancing Large Language Models through Tuning-free Rule Accumulation",
author = "Yang, Zeyuan and
Li, Peng and
Liu, Yang",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.109",
doi = "10.18653/v1/2023.emnlp-main.109",
pages = "1751--1777",
abstract = "Large Language Models (LLMs) have showcased impressive performance. However, due to their inability to capture relationships among samples, these frozen LLMs inevitably keep repeating similar mistakes. In this work, we propose our Tuning-free Rule Accumulation (TRAN) framework, which guides LLMs in improving their performance by learning from previous mistakes. Considering data arrives sequentially, LLMs gradually accumulate rules from incorrect cases, forming a rule collection. These rules are then utilized by the LLMs to avoid making similar mistakes when processing subsequent inputs. Moreover, the rules remain independent of the primary prompts, seamlessly complementing prompt design strategies. Experimentally, we show that TRAN improves over recent baselines by a large margin.",
}
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%0 Conference Proceedings
%T Failures Pave the Way: Enhancing Large Language Models through Tuning-free Rule Accumulation
%A Yang, Zeyuan
%A Li, Peng
%A Liu, Yang
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F yang-etal-2023-failures
%X Large Language Models (LLMs) have showcased impressive performance. However, due to their inability to capture relationships among samples, these frozen LLMs inevitably keep repeating similar mistakes. In this work, we propose our Tuning-free Rule Accumulation (TRAN) framework, which guides LLMs in improving their performance by learning from previous mistakes. Considering data arrives sequentially, LLMs gradually accumulate rules from incorrect cases, forming a rule collection. These rules are then utilized by the LLMs to avoid making similar mistakes when processing subsequent inputs. Moreover, the rules remain independent of the primary prompts, seamlessly complementing prompt design strategies. Experimentally, we show that TRAN improves over recent baselines by a large margin.
%R 10.18653/v1/2023.emnlp-main.109
%U https://aclanthology.org/2023.emnlp-main.109
%U https://doi.org/10.18653/v1/2023.emnlp-main.109
%P 1751-1777
Markdown (Informal)
[Failures Pave the Way: Enhancing Large Language Models through Tuning-free Rule Accumulation](https://aclanthology.org/2023.emnlp-main.109) (Yang et al., EMNLP 2023)
ACL