# Beyond Natural Language: LLMs Leveraging Alternative Formats for Enhanced Reasoning and Communication arXiv: https://arxiv.org/abs/2402.18439 ยท HTML: https://mlsanity.com/paper/2402.18439/ ## Key facts - Cited by 14 papers in the map (data through 14 Oct 2026). - Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning. - Builds on 18 of its 39 references that are in the map. - First posted on arXiv 28 Feb 2024. ## Placed in - LLM reasoning / [Reasoning for agents, planning and code](https://mlsanity.com/lr/reasoning-for-agents-planning-and-code/) ## Builds on (most cited first, 18 of 18) - [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023) - [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023) - [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023) - [WebArena: A Realistic Web Environment for Building Autonomous Agents](https://mlsanity.com/paper/2307.13854/) (2023) - [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://mlsanity.com/paper/2206.04615/) (2022) - [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://mlsanity.com/paper/2308.08155/) (2023) - [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023) - [Improving Factuality and Reasoning in Language Models through Multiagent Debate](https://mlsanity.com/paper/2305.14325/) (2023) - [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](https://mlsanity.com/paper/2303.17760/) (2023) - [GAIA: a benchmark for General AI Assistants](https://mlsanity.com/paper/2311.12983/) (2023) - [Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks](https://mlsanity.com/paper/2211.12588/) (2022) - [ChatDev: Communicative Agents for Software Development](https://mlsanity.com/paper/2307.07924/) (2023) - [Gemini: A Family of Highly Capable Multimodal Models](https://mlsanity.com/paper/2312.11805/) (2023) - [ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate](https://mlsanity.com/paper/2308.07201/) (2023) - [AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors](https://mlsanity.com/paper/2308.10848/) (2023) - [Emergent autonomous scientific research capabilities of large language models](https://mlsanity.com/paper/2304.05332/) (2023) - [PRD: Peer Rank and Discussion Improve Large Language Model based Evaluations](https://mlsanity.com/paper/2307.02762/) (2023) - [Let Models Speak Ciphers: Multiagent Debate through Embeddings](https://mlsanity.com/paper/2310.06272/) (2023) ## Built on by (most cited first, 14 of 14) - [From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agents](https://mlsanity.com/paper/2412.03563/) (2024) - [Unlocking the Capabilities of Thought: A Reasoning Boundary Framework to Quantify and Optimize Chain-of-Thought](https://mlsanity.com/paper/2410.05695/) (2024) - [Optima: Optimizing Effectiveness and Efficiency for LLM-Based Multi-Agent System](https://mlsanity.com/paper/2410.08115/) (2024) - [Text Serialization and Their Relationship with the Conventional Paradigms of Tabular Machine Learning](https://mlsanity.com/paper/2406.13846/) (2024) - [StrucText-Eval: Evaluating Large Language Model's Reasoning Ability in Structure-Rich Text](https://mlsanity.com/paper/2406.10621/) (2024) - [EcoLANG: Efficient and Effective Agent Communication Language Induction for Social Simulation](https://mlsanity.com/paper/2505.06904/) (2025) - [Efficient Multi-Agent System Training with Data Influence-Oriented Tree Search](https://mlsanity.com/paper/2502.00955/) (2025) - [HyLaT: Efficient Multi-Agent Communication via Hybrid Latent-Text Protocol](https://mlsanity.com/paper/2605.25421/) (2026) - [SIGN: Schema-Induced Games for Naming](https://mlsanity.com/paper/2510.21855/) (2025) - [Software Performance Engineering for Foundation Model-Powered Software](https://mlsanity.com/paper/2411.09580/) (2024) - [Faithful, Not Corrective: Model Capability Governs Message-Format Effects in Multi-Hop Agent Relays](https://mlsanity.com/paper/2607.09678/) (2026) - [GenCoord: Executable Commitments for Embodied LLM Agents under Private Information](https://mlsanity.com/paper/2608.22055/) (2026) - [The Communication Bottleneck: A Round-Trip Study of Tree-Structured Expression Serialization in Language Models](https://mlsanity.com/paper/2609.21509/) (2026) - [Communication between Frozen Large Language Models via Prompt Optimization in a Referential Game](https://mlsanity.com/paper/2609.31989/) (2026) ## Abstract (arXiv) 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 is released at urlhttps://github.com/thunlp/AutoForm.