Papers by Changmao Li
RAC: Efficient LLM Factuality Correction with Retrieval Augmentation (2025.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) exhibit impressive results across a wide range of tasks, yet they can often produce factually incorrect outputs. |
| Approach: | They propose a low-latency post-correction method that decomposes the LLM’s output into atomic facts and applies a fine-grained verification and correction process with retrieved content to verify and correct the Llm-generated output. |
| Outcome: | The proposed method has greatly reduced latency and token consumption up to 7x compared to previous state-of-the-art methods with similar or better performance. |
Transformers to Learn Hierarchical Contexts in Multiparty Dialogue for Span-based Question Answering (2020.acl-main)
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| Challenge: | Existing approaches to embedding in multiparty dialogues are poor for span-based question answering (QA) |
| Approach: | They propose a novel approach to transformers that learns hierarchical representations in multiparty dialogue. |
| Outcome: | The proposed model improves on the FriendsQA dataset by 3.8% and 1.4% over the two state-of-the-art models. |
Competence-Level Prediction and Resume & Job Description Matching Using Context-Aware Transformer Models (2020.emnlp-main)
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| Challenge: | a new method for resume classification reduces the time and labor needed to screen applications . the current method of screening applications involves reviewing individual resumes via string/regex matching . |
| Approach: | They propose to use transformer-based resume classification to reduce time and labor needed to screen applications. |
| Outcome: | The proposed models reduce time and labor needed to screen applications while improving the selection of suitable candidates. |