Zhichen Zeng, Qi Yu, Xiao Lin, Ruizhong Qiu, Xuying Ning, Tianxin Wei, Yuchen Yan, Jingrui He, Hanghang Tong
| Challenge: | Existing efforts to improve LLM ensemble quality have focused on model consistency, but failures are often due to heterogeneous tokenization schemes and varying model expertise. |
| Approach: | They propose a plug-and-play technique that harnesses model consistency for robust LLM ensemble. |
| Outcome: | The proposed technique improves ensemble performance and robustness against erroneous signals. |
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| Challenge: | Large language models (LLMs) are prone to inconsistencies and individual biases, limiting their reliability. |
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SCORE: Systematic COnsistency and Robustness Evaluation for Large Language Models (2025.naacl-industry)
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AdaFuse: Adaptive Ensemble Decoding for Large Language Models (2026.acl-long)
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Chengming Cui, Tianxin Wei, Ziyi Chen, Ruizhong Qiu, Zhichen Zeng, Zhining Liu, Xuying Ning, Duo Zhou, Jingrui He
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Instability in Downstream Task Performance During LLM Pretraining (2025.findings-emnlp)
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| Challenge: | a study of large language models shows that task scores fluctuate throughout training . |
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Improving Consistency in LLM Inference using Probabilistic Tokenization (2025.findings-naacl)
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