Papers by Masha Belyi
Luna: A Lightweight Evaluation Model to Catch Language Model Hallucinations with High Accuracy and Low Cost (2025.coling-industry)
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| Challenge: | Retrieval-augmented generation (RAG) systems are crucial for enhancing the capabilities of large language models (LLMs) in industry applications. |
| Approach: | They propose a DeBERTA-large encoder for hallucination detection in RAG settings that is fine-tuned for halluination detection. |
| Outcome: | The proposed model outperforms GPT-3.5 and commercial evaluation frameworks on the hallucination detection task, with 97% and 91% reduction in cost and latency, respectively. |
Personalized Dense Retrieval on Global Index for Voice-enabled Conversational Systems (2023.emnlp-industry)
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| Challenge: | Constrained retrieval is limited to entities in recent user history, which offers low coverage of future requests. |
| Approach: | They propose a personalized entity retrieval system that is robust to phonetic noise and ambiguity but is not limited to a customized index. |
| Outcome: | The proposed system corrects multiple error modes and shows 91% improvement over baseline on the entity retrieval task. |