AQA: Adaptive Question Answering in a Society of LLMs via Contextual Multi-Armed Bandit
- URL: http://arxiv.org/abs/2409.13447v2
- Date: Mon, 23 Sep 2024 08:43:06 GMT
- Title: AQA: Adaptive Question Answering in a Society of LLMs via Contextual Multi-Armed Bandit
- Authors: Mohanna Hoveyda, Arjen P. de Vries, Maarten de Rijke, Harrie Oosterhuis, Faegheh Hasibi,
- Abstract summary: In question answering (QA), different questions can be effectively addressed with different answering strategies.
We develop a dynamic method that adaptively selects the most suitable QA strategy for each question.
Our experiments show that the proposed solution is viable for adaptive orchestration of a QA system with multiple modules.
- Score: 59.10281630985958
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In question answering (QA), different questions can be effectively addressed with different answering strategies. Some require a simple lookup, while others need complex, multi-step reasoning to be answered adequately. This observation motivates the development of a dynamic method that adaptively selects the most suitable QA strategy for each question, enabling more efficient and effective systems capable of addressing a broader range of question types. To this aim, we build on recent advances in the orchestration of multiple large language models (LLMs) and formulate adaptive QA as a dynamic orchestration challenge. We define this as a contextual multi-armed bandit problem, where the context is defined by the characteristics of the incoming question and the action space consists of potential communication graph configurations among the LLM agents. We then train a linear upper confidence bound model to learn an optimal mapping between different question types and their corresponding optimal multi-LLM communication graph representation. Our experiments show that the proposed solution is viable for adaptive orchestration of a QA system with multiple modules, as it combines the superior performance of more complex strategies while avoiding their costs when simpler strategies suffice.
Related papers
- Scaling Autonomous Agents via Automatic Reward Modeling And Planning [52.39395405893965]
Large language models (LLMs) have demonstrated remarkable capabilities across a range of tasks.
However, they still struggle with problems requiring multi-step decision-making and environmental feedback.
We propose a framework that can automatically learn a reward model from the environment without human annotations.
arXiv Detail & Related papers (2025-02-17T18:49:25Z) - Dynamic Multi-Agent Orchestration and Retrieval for Multi-Source Question-Answer Systems using Large Language Models [0.0]
We propose a methodology that combines several advanced techniques in Large Language Model (LLM) retrieval to support the development of robust, multi-source question-answer systems.
This methodology is designed to integrate information from diverse data sources, through a coordinated multi-agent orchestration and dynamic retrieval approach.
Our results indicate that this approach enhances response accuracy and relevance, offering a versatile and scalable framework for developing question-answer systems.
arXiv Detail & Related papers (2024-12-23T20:28:20Z) - Progressive Multimodal Reasoning via Active Retrieval [64.74746997923967]
Multi-step multimodal reasoning tasks pose significant challenges for large language models (MLLMs)
We propose AR-MCTS, a universal framework designed to progressively improve the reasoning capabilities of MLLMs.
We show that AR-MCTS can optimize sampling diversity and accuracy, yielding reliable multimodal reasoning.
arXiv Detail & Related papers (2024-12-19T13:25:39Z) - Enhancing Multi-Step Reasoning Abilities of Language Models through Direct Q-Function Optimization [49.362750475706235]
Reinforcement Learning (RL) plays a crucial role in aligning large language models with human preferences and improving their ability to perform complex tasks.
We introduce Direct Q-function Optimization (DQO), which formulates the response generation process as a Markov Decision Process (MDP) and utilizes the soft actor-critic (SAC) framework to optimize a Q-function directly parameterized by the language model.
Experimental results on two math problem-solving datasets, GSM8K and MATH, demonstrate that DQO outperforms previous methods, establishing it as a promising offline reinforcement learning approach for aligning language models.
arXiv Detail & Related papers (2024-10-11T23:29:20Z) - Optimal Decision Making Through Scenario Simulations Using Large Language Models [0.0]
Large Language Models (LLMs) have transformed how complex problems are approached and solved.
This paper proposes an innovative approach to bridge this capability gap.
By enabling LLMs to request multiple potential options and their respective parameters from users, our system introduces a dynamic framework.
This function is designed to analyze the provided options, simulate potential outcomes, and determine the most advantageous solution.
arXiv Detail & Related papers (2024-07-09T01:23:09Z) - An Interactive Multi-modal Query Answering System with Retrieval-Augmented Large Language Models [21.892975397847316]
We present an interactive Multi-modal Query Answering (MQA) system, empowered by our newly developed multi-modal retrieval framework and navigation graph index.
One notable aspect of MQA is its utilization of contrastive learning to assess the significance of different modalities.
The system achieves efficient retrieval through our advanced navigation graph index, refined using computational pruning techniques.
arXiv Detail & Related papers (2024-07-05T02:01:49Z) - Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity [59.57065228857247]
Retrieval-augmented Large Language Models (LLMs) have emerged as a promising approach to enhancing response accuracy in several tasks, such as Question-Answering (QA)
We propose a novel adaptive QA framework, that can dynamically select the most suitable strategy for (retrieval-augmented) LLMs based on the query complexity.
We validate our model on a set of open-domain QA datasets, covering multiple query complexities, and show that ours enhances the overall efficiency and accuracy of QA systems.
arXiv Detail & Related papers (2024-03-21T13:52:30Z) - Chain-of-Discussion: A Multi-Model Framework for Complex Evidence-Based Question Answering [55.295699268654545]
We propose a novel Chain-ofDiscussion framework to leverage the synergy among open-source Large Language Models.
Our experiments show that discussions among multiple LLMs play a vital role in enhancing the quality of answers.
arXiv Detail & Related papers (2024-02-26T05:31:34Z) - In-Context Ability Transfer for Question Decomposition in Complex QA [6.745884231594893]
We propose icat (In-Context Ability Transfer) to solve complex question-answering tasks.
We transfer the ability to decompose complex questions to simpler questions or generate step-by-step rationales to LLMs.
We conduct large-scale experiments on a variety of complex QA tasks involving numerical reasoning, compositional complex QA, and heterogeneous complex QA.
arXiv Detail & Related papers (2023-10-26T11:11:07Z)
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.