Papers by Sung-Min Lee
MAFiD: Moving Average Equipped Fusion-in-Decoder for Question Answering over Tabular and Textual Data (2023.findings-eacl)
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| Challenge: | Experimental results show that Transformer-based questions have a "long" hybrid sequence over tabular and textual elements, causing long-range reasoning problems. |
| Approach: | They propose a moving average-equipped fusion-in-decoder to handle long-range reasoning problems . they use FiD and EMA to combine different levels of reasoning . |
| Outcome: | Experimental results show that the proposed model increases exact matching and F1 by 1.1 and 1.7 on the blind test set. |
RADCoT: Retrieval-Augmented Distillation to Specialization Models for Generating Chain-of-Thoughts in Query Expansion (2024.lrec-main)
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| Challenge: | Large language models (LLMs) have demonstrated superior performance to that of small language models in information retrieval for various subtasks including dense retrieval, reranking, query expansion, and pseudo-document generation. |
| Approach: | They propose a retrieval-augmented model specialization that distills the capability of LLMs to generate the chain-of-thoughts (CoT) for query expansion into a RADCoT. |
| Outcome: | The proposed model can generate the chain-of-thoughts (CoT) for query expansion, reducing the burden of internalizing and retaining world knowledge in model parameters. |