Papers by Yingtong Dou
Enhancing Foundation Models in Transaction Understanding with LLM-based Sentence Embeddings (2025.emnlp-industry)
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| Challenge: | Existing foundation models for tabular transactional data rely on index-based representations for categorical merchant fields. |
| Approach: | They propose a framework that uses LLM-generated embeddings as semantic initializations for lightweight transaction models. |
| Outcome: | The proposed framework improves performance on large transaction datasets. |
SARA: Selective and Adaptive Retrieval-augmented Generation with Context Compression (2026.acl-long)
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| Challenge: | Retrieval-augmented generation (RAG) extends large language models with external knowledge, but it must balance limited effective context, redundant retrieved evidence, and the loss of fine-grained facts. |
| Approach: | They propose a hybrid RAG framework that uses natural-language snippets and semantic compression vectors to preserve passages in text form and compress remaining evidence into interpretable vectors for iterative evidence reranking. |
| Outcome: | The proposed framework improves answer relevance, answer correctness and semantic similarity across 9 datasets and 5 open-source LLMs. |