Papers by Yuzhe Gu
ANAH: Analytical Annotation of Hallucinations in Large Language Models (2024.acl-long)
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| Challenge: | a comprehensive and fine-grained measurement of the hallucination is crucial for LLMs' wide applications. |
| Approach: | They propose a dataset that offers ANalytical Annotation of Hallucinations in Large Language Models. |
| Outcome: | The proposed dataset can be used to train and evaluate hallucination annotators. |
CompassVerifier: A Unified and Robust Verifier for LLMs Evaluation and Outcome Reward (2025.emnlp-main)
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Shudong Liu, Hongwei Liu, Junnan Liu, Linchen Xiao, Songyang Gao, Chengqi Lyu, Yuzhe Gu, Wenwei Zhang, Derek F. Wong, Songyang Zhang, Kai Chen
| Challenge: | Existing approaches lack robustness to handle complex edge cases and generalizability across different domains. |
| Approach: | They develop an accurate and lightweight verifier model for evaluation and outcome reward that matches unstructured outputs against standard answers. |
| Outcome: | The proposed model can process multiple answer types including multi-subproblems, formulas, and sequence answers while identifying abnormal/invalid responses. |
ESC: Efficient Speech Coding with Cross-Scale Residual Vector Quantized Transformers (2024.emnlp-main)
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| Challenge: | Existing neural speech codecs trade model complexity for reconstruction performance . ESC is a lightweight, parameter-efficient speech coder . |
| Approach: | They propose an efficient speech codec based on a cross-scale residual vector quantization scheme and transformers that can achieve high-fidelity speech reconstruction with significantly lower model complexity. |
| Outcome: | The proposed codec achieves high-fidelity speech reconstruction with significantly lower model complexity. |
How did we get here? Summarizing conversation dynamics (2024.naacl-long)
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Yilun Hua, Nicholas Chernogor, Yuzhe Gu, Seoyeon Jeong, Miranda Luo, Cristian Danescu-Niculescu-Mizil
| Challenge: | Throughout a conversation, the way participants interact with each other is in constant flux. |
| Approach: | They propose to summarize conversations by constructing human-written summaries and exploring automated baselines. |
| Outcome: | The summarizing tools help both humans and automated systems forecast toxic behavior in conversations. |