Exchange-of-Thought: Enhancing Large Language Model Capabilities through Cross-Model Communication (2023)

Abstract: Large Language Models (LLMs) have recently made significant strides in complex reasoning tasks through the Chain-of-Thought technique. Despite this progress, their reasoning is often constrained by their intrinsic understanding, lacking external insights. To address this, we propose Exchange-of-Thought (EoT), a novel framework that enables cross-model communication during problem-solving. Drawing inspiration from network topology, EoT integrates four unique communication paradigms: Memory, Report, Relay, and Debate. This paper delves into the communication dynamics and volume associated with each paradigm. To counterbalance the risks of incorrect reasoning chains, we implement a robust confidence evaluation mechanism within these communications. Our experiments across diverse complex reasoning tasks demonstrate that EoT significantly surpasses established baselines, underscoring the value of external insights in enhancing LLM performance. Furthermore, we show that EoT achieves these superior results in a cost-effective manner, marking a promising advancement for efficient and collaborative AI problem-solving.

## introduction large language models (llms) such as @xcite are revolutionizing the field of natural language processing (nlp) by utilizing vast training corpora and huge computational resources @xcite @xcite @xcite . although llms achieve exemplary performance across a wide range of nlp tasks @xcite , they consistently struggle to perform well in * corresponding author. reasoning tasks, and this limitation cannot be overcome solely by increasing the size of models @xcite . to overcome this shortcoming, @xcite proposed chain-of-thought (cot) prompting, which guides the model to generate a series of intermediate reasoning steps before reaching the final answer. at the same time, a series of selfcorrection methods @xcite @xcite have been proposed, which aim to iteratively improve the quality of answers by using the models' feedback on previous outputs @xcite . however, cot and self-correction solely base on the model's own understanding and perspective of the question during the reasoning process. recent studies @xcite @xcite indicate that llms struggle to revise their responses without external feedback. this can be attributed to the model's complete dependence on internal representations to generate responses, which makes it difficult to overcome inherent limitations in capability @xcite . the number of erroneous samples containing the correct answer is significantly higher than those not containing the correct answer. despite the undeniable importance of external insights @xcite , acquiring high-quality external insights remains a challenge. @xcite 's research suggests that the single reasoning chain generated by cot limits the model's reasoning performance. by increasing the temperature to sample diverse reasoning chains and selecting answers through majority voting, the model's reasoning performance can be further improved. however, when confronted with difficult questions, the model often yields a higher number of incorrect responses. in figure 2 , our analysis of correct and incorrect answers within erroneous samples from three reasoning datasets reveals that in most cases the model can deduce the correct answer. in human society, the truth, even when held by a minority, can gain widespread acceptance and recognition through clear and persuasive communication @xcite . the correct reasoning of others can serve as high-quality external insights, which enrich and enhance our collective understanding. thus, we propose exchange-of-thought (eot), a novel framework that fosters cross-model communication during the problem-solving process. this initiative enables models to incorporate the reasoning of others as external insights. figure 1 contrasts eot with cot and selfcorrection methods, highlighting the unique approach of eot in integrating external perspectives. inspired by the principles of network topology @xcite and agent communication @xcite , we propose four communication paradigms: memory, report, relay, and debate. these paradigms are designed to facilitate the exchange of ideas and reasoning chains among models, enriching the problemsolving process with a diversity of insights. fur-thermore, we delve into the intricacies of each communication paradigm, analyzing the dynamics of information flow and the volume of communication. with the awareness that both correct and incorrect reasoning chains propagate within communications, we introduce confidence evaluation mechanisms that employ the analysis of answer variations to assess models' confidence levels. it is designed to mitigate the influence of erroneous reasoning, thereby ensuring the integrity and reliability of the problem-solving process. experiments across various complex reasoning tasks demonstrate that eot significantly outperforms established strong baselines, underscoring the critical role of external insights in augmenting the capabilities of llms. we summarize our contributions as follows: ## preliminary firstly, we define the current methods that use llms to solve problems. we denote a llm with a parameter size of θ as p θ , and the sequence length as t, which includes tokens [s 1 , s 2 , . . . , s t ]. the llm predicts the next token based on the prior tokens in the sequence. the probability of the s i token is p θ (s i |s 1 , s 2 , . . . , s i-1 ). therefore, the probability of the whole sentence is p θ (s) = ∏ t @xmath0. standard prompting. standard prompting involves deriving an answer a from a question q using p θ (a|q). to bolster the comprehension of llm, an instruction prompt i can be included before the question, resulting in p θ (a|i , q). in-context learning, proposed by @xcite , aims to improve llms performance by adding demonstrations @xmath1, d 2 , . . . , d n } to the input, which can be expressed as p θ (a|d, q). cot prompting. as identified by @xcite , the incorporation of intermediate reasoning steps can improve the proficiency of llms in tackling complex reasoning challenges. to facilitate this, a rationale r i is added to examples d @xmath2, r i , a i } to guide the llms in explicitly generating reasoning steps. @xcite propose that including "let's think step by step" in the instruction i can achieve the same effect, eliminating the need for hand-crafted examples. self-consistency self-consistency proposed by @xcite , is a method that consolidates answers obtained from multiple independent reasoning chains. it returns the answer with the highest frequency of occurrence, represented as @xmath3, where f (a i ) represents the frequency of occurrence of answer a i . this approach enables the model to explore a wider array of reasoning paths, thereby enhancing its inferential capabilities. however, it remains constrained by the intrinsic limitations of llms' capabilities. ## methodology to incorporate external insights, we introduce exchange-of-thought (eot), a novel framework that facilitates communication and the exchange of ideas between models during the problem-solving process. this innovative approach leverages the communicative abilities of llms to promote the sharing of reasoning chains r and answers a among participating models, fostering a collaborative environment for thought and analysis. the implementation of eot encounters three key challenges: 1. how to identify the appropriate counterparts for model communication? 2. what are the conditions for ceasing communication between models? 3. how to minimize the influence of incorrect reasoning during the communication process? ), where k is the set from which model i can receive reasoning chains. in the first round, we use the cot method proposed by @xcite to generate (r (1) , a memory under the memory paradigm, all models record their rationale r and answer a in a logbook, which is fully visible from all models. this means that in the j-th round, any model, such as model m a , can access the reasoning chains and answers from all models (r (j-1) m , a as depicted in figure 3 , this paradigm facilitates the fastest flow of information and also incurs the highest communication cost among all paradigms. report under the report paradigm, we designate model m a as the central node, which can obtain the rationale and answer from all other models (r (j-1) m , a