@inproceedings{gao-etal-2025-expetrans,
title = "{E}xpe{T}rans: {LLM}s Are Experiential Transfer Learners",
author = "Gao, Jinglong and
Ding, Xiao and
Zou, Lingxiao and
Cai, Bibo and
Qin, Bing and
Liu, Ting",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.520/",
doi = "10.18653/v1/2025.acl-long.520",
pages = "10577--10616",
ISBN = "979-8-89176-251-0",
abstract = "Recent studies provide large language models (LLMs) with textual task-solving experiences via prompts to improve their performance.However, previous methods rely on substantial human labor or time to gather such experiences for each task, which is impractical given the growing variety of task types in user queries to LLMs.To address this issue, we design an autonomous experience transfer framework to explore whether LLMs can mimic human cognitive intelligence to autonomously transfer experience from existing source tasks to newly encountered target tasks. This not only allows the acquisition of experience without extensive costs of previous methods, but also offers a novel path for the generalization of LLMs.Experimental results on 13 datasets demonstrate that our framework effectively improves the performance of LLMs. Furthermore, we provide a detailed analysis of each module in the framework."
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<abstract>Recent studies provide large language models (LLMs) with textual task-solving experiences via prompts to improve their performance.However, previous methods rely on substantial human labor or time to gather such experiences for each task, which is impractical given the growing variety of task types in user queries to LLMs.To address this issue, we design an autonomous experience transfer framework to explore whether LLMs can mimic human cognitive intelligence to autonomously transfer experience from existing source tasks to newly encountered target tasks. This not only allows the acquisition of experience without extensive costs of previous methods, but also offers a novel path for the generalization of LLMs.Experimental results on 13 datasets demonstrate that our framework effectively improves the performance of LLMs. Furthermore, we provide a detailed analysis of each module in the framework.</abstract>
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%0 Conference Proceedings
%T ExpeTrans: LLMs Are Experiential Transfer Learners
%A Gao, Jinglong
%A Ding, Xiao
%A Zou, Lingxiao
%A Cai, Bibo
%A Qin, Bing
%A Liu, Ting
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F gao-etal-2025-expetrans
%X Recent studies provide large language models (LLMs) with textual task-solving experiences via prompts to improve their performance.However, previous methods rely on substantial human labor or time to gather such experiences for each task, which is impractical given the growing variety of task types in user queries to LLMs.To address this issue, we design an autonomous experience transfer framework to explore whether LLMs can mimic human cognitive intelligence to autonomously transfer experience from existing source tasks to newly encountered target tasks. This not only allows the acquisition of experience without extensive costs of previous methods, but also offers a novel path for the generalization of LLMs.Experimental results on 13 datasets demonstrate that our framework effectively improves the performance of LLMs. Furthermore, we provide a detailed analysis of each module in the framework.
%R 10.18653/v1/2025.acl-long.520
%U https://aclanthology.org/2025.acl-long.520/
%U https://doi.org/10.18653/v1/2025.acl-long.520
%P 10577-10616
Markdown (Informal)
[ExpeTrans: LLMs Are Experiential Transfer Learners](https://aclanthology.org/2025.acl-long.520/) (Gao et al., ACL 2025)
ACL
- Jinglong Gao, Xiao Ding, Lingxiao Zou, Bibo Cai, Bing Qin, and Ting Liu. 2025. ExpeTrans: LLMs Are Experiential Transfer Learners. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 10577–10616, Vienna, Austria. Association for Computational Linguistics.