Papers by Radu State
HalluGuard: Evidence-Grounded Small Reasoning Models to Mitigate Hallucinations in Retrieval-Augmented Generation (2026.findings-acl)
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| Challenge: | Large Language Models excel at NLP tasks but remain prone to hallucinations . small language models can achieve competitive results in specific tasks . |
| Approach: | They propose a 4B-parameter Small Reasoning Model (SRM) that can be used to classify document-claim pairs as grounded or hallucinated in closed-book, document-grounded settings. |
| Outcome: | The proposed model achieves 84.4% balanced accuracy on the RAGTruth subset of the LLM-AggreFact benchmark, surpassing specialized models, MiniCheck (7B; 84.0%) and Granite Guardian 3.3 (82.2%) Across the benchmark, it reaches 77.1% BAcc, surpasses larger general-purpose LLMs such as GPT-4o (75.9%). |
Small Language Models in the Real World: Insights from Industrial Text Classification (2025.acl-industry)
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| Challenge: | With the emergence of ChatGPT, transformer-only models have significantly advanced text classification and related tasks. |
| Approach: | They propose to use prompt engineering and supervised fine-tuning methods for transformer-based text classification in industrial applications. |
| Outcome: | The proposed models perform well in a variety of industrial scenarios, including email classification, legal document categorization, and the classification of extremely long academic texts. |