2 citations · 3 across the 4 of their papers we have counts for
4 papers
C2PO: Diagnosing and Disentangling Bias Shortcuts in LLMs
Xuan Feng, Bo An, Tianlong Gu +4
Bias in Large Language Models (LLMs) poses significant risks to trustworthiness, manifesting primarily as stereotypical biases (e.g., gender or racial stereotypes) and structural b…
Physics-Informed Autonomous LLM Agents for Explainable Power Electronics Modulation Design
Junhua Liu, Fanfan Lin, Xinze Li +2
LLM-based autonomous agents have recently shown strong capabilities in solving complex industrial design tasks. However, in domains aiming for carbon neutrality and high-performanc…
Evaluating the Correctness of Inference Patterns Used by LLMs for Judgment
Lu Chen, Yuxuan Huang, Yixing Li +6
This paper presents a method to analyze the inference patterns used by Large Language Models (LLMs) for judgment in a case study on legal LLMs, so as to identify potential incorrec…
PE-GPT: A Physics-Informed Interactive Large Language Model for Power Converter Modulation Design
Fanfan Lin, Junhua Liu, Xinze Li +4
This paper proposes PE-GPT, a custom-tailored large language model uniquely adapted for power converter modulation design. By harnessing in-context learning and specialized tiered…