Papers by Sangdon Park
TRAQ: Trustworthy Retrieval Augmented Question Answering via Conformal Prediction (2024.naacl-long)
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| Challenge: | Large language models (LLMs) often generate incorrect responses based on made-up facts, which are called hallucinations. |
| Approach: | They propose a framework that combines Retrieval Augmented Generation with conformal prediction to provide the first end-to-end statistical correctness guarantee for RAG. |
| Outcome: | The proposed framework reduces prediction set size by 16.2% on average compared to an ablation. |
ChronoBias: A Benchmark for Evaluating Temporal Group Bias in the Time-sensitive Knowledge of Large Language Models (2025.findings-emnlp)
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| Challenge: | Using a template-based semi-automated generation method, we evaluate time-conditional group bias in time-sensitive knowledge of large language models (LLMs). |
| Approach: | They propose a template-based semi-automated generation method to construct a time-conditional group bias benchmark. |
| Outcome: | The proposed method balancing quality-quantity trade-off in existing benchmark curation approaches. |
Retrieval-Augmented Generation with Estimation of Source Reliability (2025.emnlp-main)
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| Challenge: | Retrieval-Augmented Generation (RAG) is an effective approach to enhance the factual accuracy of large language models (LLMs). |
| Approach: | They propose a multi-source RAG framework that estimates the reliability of sources and prioritizes highly reliable and relevant documents. |
| Outcome: | The proposed framework outperforms baselines in scenarios with heterogeneous source reliability while scaling efficiently as the number of sources increases. |