Agent-Based Detection and Resolution of Incompleteness and Ambiguity in Interactions with Large Language Models

• URL: http://arxiv.org/abs/2507.03726v1
• Date: Fri, 04 Jul 2025 17:28:33 GMT
• Title: Agent-Based Detection and Resolution of Incompleteness and Ambiguity in Interactions with Large Language Models
• Authors: Riya Naik, Ashwin Srinivasan, Swati Agarwal, Estrid He,
• Abstract summary: This paper examines the use of agent-based architecture to bolster LLM-based Question-Answering systems with additional reasoning capabilities.<n>We equip different LLMs with agents that act as specialists in detecting and resolving deficiencies of incompleteness and ambiguity.<n>Suggesting the agent-based approach could be a useful mechanism to harness the power of LLMs to develop more robust QA systems.
• Score: 0.9856777842758593
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many of us now treat LLMs as modern-day oracles asking it almost any kind of question. However, consulting an LLM does not have to be a single turn activity. But long multi-turn interactions can get tedious if it is simply to clarify contextual information that can be arrived at through reasoning. In this paper, we examine the use of agent-based architecture to bolster LLM-based Question-Answering systems with additional reasoning capabilities. We examine the automatic resolution of potential incompleteness or ambiguities in questions by transducers implemented using LLM-based agents. We focus on several benchmark datasets that are known to contain questions with these deficiencies to varying degrees. We equip different LLMs (GPT-3.5-Turbo and Llama-4-Scout) with agents that act as specialists in detecting and resolving deficiencies of incompleteness and ambiguity. The agents are implemented as zero-shot ReAct agents. Rather than producing an answer in a single step, the model now decides between 3 actions a) classify b) resolve c) answer. Action a) decides if the question is incomplete, ambiguous, or normal. Action b) determines if any deficiencies identified can be resolved. Action c) answers the resolved form of the question. We compare the use of LLMs with and without the use of agents with these components. Our results show benefits of agents with transducer 1) A shortening of the length of interactions with human 2) An improvement in the answer quality and 3) Explainable resolution of deficiencies in the question. On the negative side we find while it may result in additional LLM invocations and in some cases, increased latency. But on tested datasets, the benefits outweigh the costs except when questions already have sufficient context. Suggesting the agent-based approach could be a useful mechanism to harness the power of LLMs to develop more robust QA systems.

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