most citedTake Care of Your Prompt Bias! Investigating and Mitigating Prompt Bias in Factual Knowledge Extraction

4 citations · 4 across the 2 of their papers we have counts for

collaborators

5 papers

cs.CY2025

Patterns and Purposes: A Cross-Journal Analysis of AI Tool Usage in Academic Writing

Ziyang Xu

This study investigates the use of AI tools in academic writing through an analysis of AI usage declarations in journals. Using a mixed-methods approach combining content analysis,…

q-bio.BM2024

Biology-Instructions: A Dataset and Benchmark for Multi-Omics Sequence Understanding Capability of Large Language Models

Haonan He, Yuchen Ren, Yining Tang +12

Large language models (LLMs) have shown remarkable capabilities in general domains, but their application to multi-omics biology remains underexplored. To address this gap, we intr…

cs.CL2024

Achieving >97% on GSM8K: Deeply Understanding the Problems Makes LLMs Better Solvers for Math Word Problems

Qihuang Zhong, Kang Wang, Ziyang Xu +3

Chain-of-Thought (CoT) prompting has enhanced the performance of Large Language Models (LLMs) across various reasoning tasks. However, CoT still falls short in dealing with complex…

cs.CL20244 cited

Take Care of Your Prompt Bias! Investigating and Mitigating Prompt Bias in Factual Knowledge Extraction

Ziyang Xu, Keqin Peng, Liang Ding +2

Recent research shows that pre-trained language models (PLMs) suffer from "prompt bias" in factual knowledge extraction, i.e., prompts tend to introduce biases toward specific labe…

physics.plasm-ph2024

RHDLPP: A multigroup radiation hydrodynamics code for laser-produced plasmas

Qi Min, Ziyang Xu, Siqi He +9

We introduce the RHDLPP, a flux-limited multigroup radiation hydrodynamics numerical code designed for simulating laser-produced plasmas in diverse environments. The code bifurcate…