Papers by Weijing Huang
Bridging the Memorization-Utilization Gap: Near-Lossless Context Compression via Reinforcement Learning (2026.acl-long)
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| Challenge: | Recent advances in context compression have failed to effectively utilize compressed representations for downstream tasks. |
| Approach: | They propose a holistic training paradigm that uses outcome-based RL to enable implicit expansion. |
| Outcome: | The proposed model outperforms previous models on NIAH, LongBench and multi-hop reasoning. |
PhraseCTM: Correlated Topic Modeling on Phrases within Markov Random Fields (P18-2)
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| Challenge: | Recent phrase-level topic models are unable to capture the correlation structure among the discovered topics. |
| Approach: | They propose a phrase-level topic model PhraseCTM and a method to find out the correlations of topics at phrase level. |
| Outcome: | The proposed method shows that correlated topic modeling is a good way to interpret themes of corpus. |
Training Medical QA Models Based on Mixed Rewards from Multiple-Choice and Open-Ended Questions (2025.findings-emnlp)
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| Challenge: | Reinforcement learning (RL) for large language models typically requires clear reward signals, which are often unavailable for open-ended (OE) questions where answer evaluation is ambiguous without scalable expert labeling. |
| Approach: | They propose a mixed-data approach to training large language models with varying reward clarity . they combine Multiple-choice questions (MCQs) with OE questions for which they use simpler, potentially noisy rewards such as Jaccard similarity or LLM-based evaluators. |
| Outcome: | The mixed-data approach improves medical question-answering performance across model scales. |
LEAF: Learning and Evaluation Augmented by Fact-Checking to Improve Factualness in Large Language Models (2025.emnlp-industry)
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| Challenge: | Large language models (LLMs) struggle with factual accuracy in knowledge-intensive domains like healthcare. |
| Approach: | They propose a framework for improving LLM factuality in medical question answering . RAFE, Fact-Check-then-RAG and Learning from Fact Check are components . |
| Outcome: | Experimental results show that LEAF outperforms Factcheck-GPT in detecting inaccuracies and corrects errors without labeling . the framework provides a scalable solution for industrial applications requiring high factuality scores. |