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[2602.18137] Agentic Adversarial QA for Improving Domain-Specific LLMs
Summary
The paper presents an adversarial question-generation framework aimed at enhancing the performance of domain-specific large language models (LLMs) by addressing their limitations in interpretive reasoning and sample efficiency.
Why It Matters
As LLMs are increasingly used in specialized fields, improving their adaptability through effective data generation methods is crucial. This research addresses the challenges of data scarcity and model performance, making it relevant for AI development in niche domains.
Key Takeaways
- Proposes a novel adversarial question-generation framework.
- Addresses the shortcomings of existing synthetic data generation methods.
- Demonstrates improved accuracy with fewer synthetic samples on specialized datasets.
Computer Science > Computation and Language arXiv:2602.18137 (cs) [Submitted on 20 Feb 2026] Title:Agentic Adversarial QA for Improving Domain-Specific LLMs Authors:Vincent Grari, Ciprian Tomoiaga, Sylvain Lamprier, Tatsunori Hashimoto, Marcin Detyniecki View a PDF of the paper titled Agentic Adversarial QA for Improving Domain-Specific LLMs, by Vincent Grari and 4 other authors View PDF HTML (experimental) 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 docume...
