@inproceedings{chen-etal-2024-beyond-natural,
title = "Beyond Natural Language: {LLM}s Leveraging Alternative Formats for Enhanced Reasoning and Communication",
author = "Chen, Weize and
Yuan, Chenfei and
Yuan, Jiarui and
Su, Yusheng and
Qian, Chen and
Yang, Cheng and
Xie, Ruobing and
Liu, Zhiyuan and
Sun, Maosong",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.623",
pages = "10626--10641",
abstract = "Natural language (NL) has long been the predominant format for human cognition and communication, and by extension, has been similarly pivotal in the development and application of Large Language Models (LLMs). Yet, besides NL, LLMs have seen various non-NL formats during pre-training, such as code and logical expression. NL{'}s status as the optimal format for LLMs, particularly in single-LLM reasoning and multi-agent communication, has not been thoroughly examined. In this work, we challenge the default use of NL by exploring the utility of non-NL formats in these contexts. We show that allowing LLMs to autonomously select the most suitable format before reasoning or communicating leads to a 3.3 to 5.7{\%} improvement in reasoning efficiency for different LLMs, and up to a 72.7{\%} reduction in token usage in multi-agent communication, all while maintaining communicative effectiveness. Our comprehensive analysis further reveals that LLMs can devise a format from limited task instructions and that the devised format is effectively transferable across different LLMs. Intriguingly, the structured communication format decided by LLMs exhibits notable parallels with established agent communication languages, suggesting a natural evolution towards efficient, structured communication in agent communication. Our code will be released to facilitate further exploration.",
}
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<abstract>Natural language (NL) has long been the predominant format for human cognition and communication, and by extension, has been similarly pivotal in the development and application of Large Language Models (LLMs). Yet, besides NL, LLMs have seen various non-NL formats during pre-training, such as code and logical expression. NL’s status as the optimal format for LLMs, particularly in single-LLM reasoning and multi-agent communication, has not been thoroughly examined. In this work, we challenge the default use of NL by exploring the utility of non-NL formats in these contexts. We show that allowing LLMs to autonomously select the most suitable format before reasoning or communicating leads to a 3.3 to 5.7% improvement in reasoning efficiency for different LLMs, and up to a 72.7% reduction in token usage in multi-agent communication, all while maintaining communicative effectiveness. Our comprehensive analysis further reveals that LLMs can devise a format from limited task instructions and that the devised format is effectively transferable across different LLMs. Intriguingly, the structured communication format decided by LLMs exhibits notable parallels with established agent communication languages, suggesting a natural evolution towards efficient, structured communication in agent communication. Our code will be released to facilitate further exploration.</abstract>
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%0 Conference Proceedings
%T Beyond Natural Language: LLMs Leveraging Alternative Formats for Enhanced Reasoning and Communication
%A Chen, Weize
%A Yuan, Chenfei
%A Yuan, Jiarui
%A Su, Yusheng
%A Qian, Chen
%A Yang, Cheng
%A Xie, Ruobing
%A Liu, Zhiyuan
%A Sun, Maosong
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F chen-etal-2024-beyond-natural
%X Natural language (NL) has long been the predominant format for human cognition and communication, and by extension, has been similarly pivotal in the development and application of Large Language Models (LLMs). Yet, besides NL, LLMs have seen various non-NL formats during pre-training, such as code and logical expression. NL’s status as the optimal format for LLMs, particularly in single-LLM reasoning and multi-agent communication, has not been thoroughly examined. In this work, we challenge the default use of NL by exploring the utility of non-NL formats in these contexts. We show that allowing LLMs to autonomously select the most suitable format before reasoning or communicating leads to a 3.3 to 5.7% improvement in reasoning efficiency for different LLMs, and up to a 72.7% reduction in token usage in multi-agent communication, all while maintaining communicative effectiveness. Our comprehensive analysis further reveals that LLMs can devise a format from limited task instructions and that the devised format is effectively transferable across different LLMs. Intriguingly, the structured communication format decided by LLMs exhibits notable parallels with established agent communication languages, suggesting a natural evolution towards efficient, structured communication in agent communication. Our code will be released to facilitate further exploration.
%U https://aclanthology.org/2024.findings-emnlp.623
%P 10626-10641
Markdown (Informal)
[Beyond Natural Language: LLMs Leveraging Alternative Formats for Enhanced Reasoning and Communication](https://aclanthology.org/2024.findings-emnlp.623) (Chen et al., Findings 2024)
ACL
- Weize Chen, Chenfei Yuan, Jiarui Yuan, Yusheng Su, Chen Qian, Cheng Yang, Ruobing Xie, Zhiyuan Liu, and Maosong Sun. 2024. Beyond Natural Language: LLMs Leveraging Alternative Formats for Enhanced Reasoning and Communication. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 10626–10641, Miami, Florida, USA. Association for Computational Linguistics.
