4 citations · 4 across the 4 of their papers we have counts for
7 papers
Suppressing Pressure, Amplifying Evidence: Self-Guided Attention Steering to Mitigate Sycophancy and Stubbornness
Yinghao He, Mengyu Xu, Haixiang Sun +8
Reliable language models should resist unsupported user pressure while effectively using objective contextual information. However, models may exhibit sycophancy by yielding to uns…
Positive-Unlabeled Learning for Agent Safety False Alarm Auditing
Xichen Yan, Chongyang Gao, Kezhen Chen +3
Safety monitors help safeguard language-model agents interacting with external tools and environments, but conservative monitoring can generate many false alarms, consuming extensi…
On Unlearning for Time-series Forecasting
Zeyu Shi, Yanhui Luo, Ziming Hong +4
Time-series forecasting is widely used in sensitive domains. Models in these settings are often trained on longitudinal user- or entity-level records, which may later require remov…
FM-Bench: A Benchmark for Long-Horizon Management with Competing Agents
Tianyou Wang, Chongyang Gao, Kezhen Chen +6
Language model agents now execute bounded tasks reliably. Whether they can sustain effective decision-making over long horizons, where actions have cumulative consequences and the…
Insight Miner: A Time Series Analysis Dataset for Cross-Domain Alignment with Natural Language
Yunkai Zhang, Yawen Zhang, Ming Zheng +10
Time-series data is critical across many scientific and industrial domains, including environmental analysis, agriculture, transportation, and finance. However, mining insights fro…
AlphaLoRA: Assigning LoRA Experts Based on Layer Training Quality
Peijun Qing, Chongyang Gao, Yefan Zhou +3
Parameter-efficient fine-tuning methods, such as Low-Rank Adaptation (LoRA), are known to enhance training efficiency in Large Language Models (LLMs). Due to the limited parameters…
Higher Layers Need More LoRA Experts
Chongyang Gao, Kezhen Chen, Jinmeng Rao +7
Parameter-efficient tuning (PEFT) techniques like low-rank adaptation (LoRA) offer training efficiency on Large Language Models, but their impact on model performance remains limit…