S$^2$-MAD: Breaking the Token Barrier to Enhance Multi-Agent Debate Efficiency
- URL: http://arxiv.org/abs/2502.04790v2
- Date: Thu, 10 Apr 2025 02:29:35 GMT
- Title: S$^2$-MAD: Breaking the Token Barrier to Enhance Multi-Agent Debate Efficiency
- Authors: Yuting Zeng, Weizhe Huang, Lei Jiang, Tongxuan Liu, Xitai Jin, Chen Tianying Tiana, Jing Li, Xiaohua Xu,
- Abstract summary: Multi-agent Debate (MAD) has emerged as a viable approach for enhancing the reasoning capabilities of large language models (LLMs)<n>We introduce a novel sparsification strategy designed to reduce token costs within MAD.<n>This approach minimizes ineffective exchanges of information and unproductive discussions among agents, thereby enhancing the overall efficiency of the debate process.
- Score: 5.195584743414427
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Large language models (LLMs) have demonstrated remarkable capabilities across various natural language processing (NLP) scenarios, but they still face challenges when handling complex arithmetic and logical reasoning tasks. While Chain-Of-Thought (CoT) reasoning, self-consistency (SC) and self-correction strategies have attempted to guide models in sequential, multi-step reasoning, Multi-agent Debate (MAD) has emerged as a viable approach for enhancing the reasoning capabilities of LLMs. By increasing both the number of agents and the frequency of debates, the performance of LLMs improves significantly. However, this strategy results in a significant increase in token costs, presenting a barrier to scalability. To address this challenge, we introduce a novel sparsification strategy designed to reduce token costs within MAD. This approach minimizes ineffective exchanges of information and unproductive discussions among agents, thereby enhancing the overall efficiency of the debate process. We conduct comparative experiments on multiple datasets across various models, demonstrating that our approach significantly reduces the token costs in MAD to a considerable extent. Specifically, compared to MAD, our approach achieves an impressive reduction of up to 94.5\% in token costs while maintaining performance degradation below 2.0\%.
Related papers
- CortexDebate: Debating Sparsely and Equally for Multi-Agent Debate [11.155092859033784]
Multi-Agent Debate (MAD) has emerged as an effective strategy to mitigate issues with single Large Language Model (LLM)<n>Existing MAD methods face two major issues: (a) too lengthy input contexts, which causes LLM agents to get lost in plenty of input information and experiences performance drop; and (b) the overconfidence dilemma, where self-assured LLM agents dominate the debate, leading to low debating effectiveness.<n>We propose a novel MAD method called "CortexDebate", inspired by the human brain's tendency to establish a sparse and dynamically optimized network among cortical areas governed by white matter.
arXiv Detail & Related papers (2025-07-05T07:23:15Z) - Exploring and Exploiting the Inherent Efficiency within Large Reasoning Models for Self-Guided Efficiency Enhancement [101.77467538102924]
Large reasoning models (LRMs) exhibit overthinking, which hinders efficiency and inflates inference cost.<n>We propose two lightweight methods to enhance LRM efficiency.<n>First, we introduce Efficiency Steering, a training-free activation steering technique that modulates reasoning behavior via a single direction.<n>Second, we develop Self-Rewarded Efficiency RL, a reinforcement learning framework that dynamically balances task accuracy and brevity.
arXiv Detail & Related papers (2025-06-18T17:18:12Z) - Truth in the Few: High-Value Data Selection for Efficient Multi-Modal Reasoning [71.3533541927459]
We propose a novel data selection paradigm termed Activation Reasoning Potential (RAP)<n>RAP identifies cognitive samples by estimating each sample's potential to stimulate genuine multi-modal reasoning.<n>Our RAP method consistently achieves superior performance using only 9.3% of the training data, while reducing computational costs by over 43%.
arXiv Detail & Related papers (2025-06-05T08:40:24Z) - Revisiting Multi-Agent Debate as Test-Time Scaling: A Systematic Study of Conditional Effectiveness [50.29739337771454]
Multi-agent debate (MAD) approaches offer improved reasoning, robustness, and diverse perspectives over monolithic models.<n>This paper conceptualizes MAD as a test-time computational scaling technique, distinguished by collaborative refinement and diverse exploration capabilities.<n>We conduct a comprehensive empirical investigation comparing MAD with strong self-agent test-time scaling baselines on mathematical reasoning and safety-related tasks.
arXiv Detail & Related papers (2025-05-29T01:02:55Z) - Do We Truly Need So Many Samples? Multi-LLM Repeated Sampling Efficiently Scales Test-Time Compute [55.330813919992465]
This paper presents a simple, effective, and cost-efficient strategy to improve LLM performance by scaling test-time compute.
Our strategy builds upon the repeated-sampling-then-voting framework, with a novel twist: incorporating multiple models, even weaker ones, to leverage their complementary strengths.
arXiv Detail & Related papers (2025-04-01T13:13:43Z) - Efficient Inference for Large Reasoning Models: A Survey [42.61170621552432]
Large Reasoning Models (LRMs) significantly improve the reasoning ability of Large Language Models (LLMs) by learning to reason.
However, their deliberative reasoning process leads to inefficiencies in token usage, memory consumption, and inference time.
This survey provides a review of efficient inference methods designed specifically for LRMs, focusing on mitigating token inefficiency while preserving the reasoning quality.
arXiv Detail & Related papers (2025-03-29T13:27:46Z) - Stepwise Perplexity-Guided Refinement for Efficient Chain-of-Thought Reasoning in Large Language Models [56.37421741507468]
Chain-of-Thought (CoT) reasoning has significantly enhanced the performance of large language models (LLMs)
We propose a method to identify critical reasoning steps using perplexity as a measure of their importance.
arXiv Detail & Related papers (2025-02-18T20:04:51Z) - DSMoE: Matrix-Partitioned Experts with Dynamic Routing for Computation-Efficient Dense LLMs [70.91804882618243]
This paper proposes DSMoE, a novel approach that achieves sparsification by partitioning pre-trained FFN layers into computational blocks.
We implement adaptive expert routing using sigmoid activation and straight-through estimators, enabling tokens to flexibly access different aspects of model knowledge.
Experiments on LLaMA models demonstrate that under equivalent computational constraints, DSMoE achieves superior performance compared to existing pruning and MoE approaches.
arXiv Detail & Related papers (2025-02-18T02:37:26Z) - Self-Regulation and Requesting Interventions [63.5863047447313]
We propose an offline framework that trains a "helper" policy to request interventions.<n>We score optimal intervention timing with PRMs and train the helper model on these labeled trajectories.<n>This offline approach significantly reduces costly intervention calls during training.
arXiv Detail & Related papers (2025-02-07T00:06:17Z) - Token-Budget-Aware LLM Reasoning [33.81357562939748]
Chain-of-Thought (CoT) reasoning incurs significant overhead in token usage.
We propose a token-budget-aware LLM reasoning framework.
Our method effectively reduces token costs in CoT reasoning with only a slight performance reduction.
arXiv Detail & Related papers (2024-12-24T16:55:45Z) - Less is More: A Simple yet Effective Token Reduction Method for Efficient Multi-modal LLMs [14.533229831531168]
We introduce a new approach, Token Reduction using CLIP Metric (TRIM), aimed at improving the efficiency of MLLMs without sacrificing their performance.<n>Inspired by human attention patterns in Visual Question Answering (VQA) tasks, TRIM presents a fresh perspective on the selection and reduction of image tokens.<n>The results demonstrate a significant reduction in computational overhead while maintaining a consistent level of performance.
arXiv Detail & Related papers (2024-09-17T08:56:27Z) - Strategic Chain-of-Thought: Guiding Accurate Reasoning in LLMs through Strategy Elicitation [16.350747493026432]
The Chain-of-Thought (CoT) paradigm has emerged as a critical approach for enhancing the reasoning capabilities of large language models (LLMs)
We propose the textbfStrategic Chain-of-Thought (SCoT) to refine LLM performance by integrating strategic knowledge prior to generating intermediate reasoning steps.
SCoT employs a two-stage approach within a single prompt: first eliciting an effective problem-solving strategy, which is then used to guide the generation of high-quality CoT paths and final answers.
arXiv Detail & Related papers (2024-09-05T06:28:05Z) - STBA: Towards Evaluating the Robustness of DNNs for Query-Limited Black-box Scenario [50.37501379058119]
We propose the Spatial Transform Black-box Attack (STBA) to craft formidable adversarial examples in the query-limited scenario.
We show that STBA could effectively improve the imperceptibility of the adversarial examples and remarkably boost the attack success rate under query-limited settings.
arXiv Detail & Related papers (2024-03-30T13:28:53Z) - Exploiting Activation Sparsity with Dense to Dynamic-k Mixture-of-Experts Conversion [4.716845031095804]
Transformer models can face practical limitations due to their high computational requirements.
Such models exhibit significant activation sparsity, which can be leveraged to reduce the inference cost by converting parts of the network into equivalent Mixture-of-Experts (MoE) layers.
We demonstrate that the efficiency of the conversion can be significantly enhanced by a proper regularization of the activation sparsity of the base model.
arXiv Detail & Related papers (2023-10-06T16:34:51Z) - OverPrompt: Enhancing ChatGPT through Efficient In-Context Learning [49.38867353135258]
We propose OverPrompt, leveraging the in-context learning capability of LLMs to handle multiple task inputs.
Our experiments show that OverPrompt can achieve cost-efficient zero-shot classification without causing significant detriment to task performance.
arXiv Detail & Related papers (2023-05-24T10:08:04Z) - Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models [81.01397924280612]
Large language models (LLMs) can achieve highly effective performance on various reasoning tasks by incorporating step-by-step chain-of-thought (CoT) prompting as demonstrations.
We introduce Iter-CoT (Iterative bootstrapping in Chain-of-Thoughts Prompting), an iterative bootstrapping approach for selecting exemplars and generating reasoning chains.
arXiv Detail & Related papers (2023-04-23T13:54:39Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.