Beyond Natural Language: LLM s Leveraging Alternative Formats for Enhanced Reasoning and Communication (2024)

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.

## introduction natural language (nl) has long been recognized as a fundamental format for human thought expression and communication, underscored by its pivotal role in the cognitive processes and information exchange of humans @xcite @xcite . however, the human mind's capabilities often extend beyond the scope of nl, as suggested by the concept of mentalese, a mental language posited by linguists @xcite . recent advancements in llms @xcite @xcite have been remarkable, leading to their increasingly sophisticated application in language agents @xcite @xcite . these advancements, while impressive, predominantly utilize nl for both single-llm reasoning via chain-of-thought (cot) @xcite and multi-agent communication @xcite . given the human mind's proficiency in transcending nl, critical inquiries emerge: is nl the optimal format for llms in reasoning and inter-agent communication? if not, how should we determine the most suitable format for these applications (fig. 1 )? recent research challenges the notion that nl is the ideal intermediate format for llm reasoning and multi-agent communication. emerging variants of cot, such as program-of-thought @xcite and x-of-thought @xcite have explored the use of alternative formats like code and mathematical equations, expanding the llms' reasoning capabilities. however, these approaches often integrate external tools, where the alternative formats primarily serve as a means to facilitate tool execution (e.g., prompting llm to generate code and use code interpreter execution result as the answer). this introduces complexity in discerning whether the performance improvements are attributable to the format itself or the accompanying tools. additionally, while the natural ambiguities and emotions inherent in nl may align well with the nuances of human communication, these may not be desired in agent communication, where precision is more important. nonetheless, current multi-agent research predominantly utilizes nl @xcite @xcite , with limited exploration of other potentially more accurate and efficient communication formats. in this study, we implement a straightforward and effective mechanism that prompts the model to favor non-nl formats for single-llm reasoning and multi-agent communication tasks. by adding an instruction to the original cot prompt that directs llms to explore a non-nl format appropriate for the current input, we showcase the llms' capacity for autonomous format decisions. we observe that the llms can leverage many non-nl formats such as ordered lists, logical expressions, and markdown tables to reason better. also, we observe that agents can use more structured language as their communication language to enhance the efficiency of multi-agent collaboration. in particular, our analyses across various single-llm reasoning tasks demonstrate an average improvement in performance by 3.3-5.7%. for multi-agent communication, we observe a reduction in token usage by up to 72.7% without sacrificing effectiveness. these results highlight the considerable potential of non-nl formats in amplifying the reasoning capabilities and communicative efficiency of llms. our investigation further extends to a comprehensive analysis revealing that llms can devise a suitable format from a set of task-specific examples. using the fixed devised format for the whole task also leads to better answers. moreover, we show that the formats devised by one llm are transferable to another llm. and for the multi-agent communication format, we find that the format adopted by llms mirrors those of traditional agent communication languages (acls) like kqml @xcite , highlighting their clarity, brevity, and structured format for efficient exchanges. empirically, our approach significantly reduces token usage compared to both acls and nl without sacrificing performance. our work underscores the efficacy of non-nl formats in advancing llm reasoning and communication. ## conclusion in this work, we demonstrate that llms can autonomously determine suitable non-nl formats for reasoning and communication using the aut-oform prompting method. our analyses address six key research questions, showing that llms can generalize a reasoning format from task-specific examples and transfer it across different models. additionally, the communication formats generated by llms resemble traditional acls, offering both precision and efficiency. these insights enhance our understanding of llms' capabilities beyond nl, improving llm reasoning and interagent communication.