Agentic Adversarial QA for Improving Domain-Specific LLMs

2026-02-20

AI Summary

This work addresses the scarcity of high-quality annotated data for domain-specific large language models, a limitation exacerbated by the inadequate reasoning capabilities and sample inefficiency of existing synthetic data generation methods. To overcome this, the authors propose an adversarial question generation framework that introduces, for the first time, an agent-based adversarial mechanism. This approach iteratively synthesizes questions that are semantically challenging, highly informative, and minimally redundant by contrasting the outputs of a model under optimization against those of an expert model guided by reference documents. Integrating expert guidance, iterative feedback, and document-level semantic alignment, the method substantially enhances both explanatory reasoning ability and data efficiency. Evaluated on a specialized subset of LegalBench, the framework achieves higher accuracy with only a small number of synthetic samples, demonstrating its effectiveness and efficiency.

Technology Category

• Natural Language Processing: Question Answering
• Multiagent Systems: Adversarial Agents
• Machine Learning: Adversarial Learning & Robustness

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

Large Language Models (LLMs), despite extensive pretraining on broad internet corpora, often struggle to adapt effectively to specialized domains. There is growing interest in fine-tuning these models for such domains; however, progress is constrained by the scarcity and limited coverage of high-quality, task-relevant data. To address this, synthetic data generation methods such as paraphrasing or knowledge extraction are commonly applied. Although these approaches excel at factual recall and conceptual knowledge, they suffer from two critical shortcomings: (i) they provide minimal support for interpretive reasoning capabilities in these specialized domains, and (ii) they often produce synthetic corpora that are excessively large and redundant, resulting in poor sample efficiency. To overcome these gaps, we propose an adversarial question-generation framework that produces a compact set of semantically challenging questions. These questions are constructed by comparing the outputs of the model to be adapted and a robust expert model grounded in reference documents, using an iterative, feedback-driven process designed to reveal and address comprehension gaps. Evaluation on specialized subsets of the LegalBench corpus demonstrates that our method achieves greater accuracy with substantially fewer synthetic samples.

Problem

• domain-specific LLMs
• synthetic data generation
• interpretive reasoning
• sample efficiency
• data scarcity

Innovation

• adversarial question generation
• domain-specific LLMs
• synthetic data efficiency
• interpretive reasoning
• expert-guided feedback