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National Defense University

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Research library5linked papers
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Selected work

Representative Papers

Analysis of LLM Bias (Chinese Propaganda&Anti-US Sentiment) in DeepSeek-R1 vs. ChatGPT o3-mini-high

Jun 02, 2025

This study systematically evaluates the ideological neutrality of large language models (LLMs) within U.S.–China political contexts, comparing China-aligned DeepSeek-R1 and non-China-aligned ChatGPT o3-mini-high on state propaganda and anti-American sentiment. We propose the first cross-lingual (Simplified Chinese, Traditional Chinese, English), decontextualized bias evaluation framework, constructing a 1,200-item multilingual reasoning benchmark. Evaluation combines rubric-guided GPT-4o automated scoring with double-blind human annotation. Results reveal a pronounced “invisible amplifier” effect in DeepSeek-R1: its pro-state and anti-American biases are strongest in Simplified Chinese, attenuate sharply across linguistic shifts (→ Traditional Chinese → English), and generalize beyond politics into cultural domains; ChatGPT o3-mini-high remains largely ideologically neutral. The findings expose a deep coupling between linguistic representation and geopolitical alignment, offering a novel paradigm for assessing value alignment in LLMs.

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A Double Inertial Forward-Backward Splitting Algorithm With Applications to Regression and Classification Problems

May 01, 2025

This paper addresses the problem of finding zeros of the sum of a co-coercive operator and a maximally monotone operator in real Hilbert spaces—a formulation that unifies various regression and classification tasks. To this end, we propose a novel doubly inertial forward–backward splitting algorithm, the first to incorporate two independent, tunable inertia parameters. Crucially, this design accelerates convergence and enhances numerical stability without incurring additional computational cost. Under standard assumptions of monotonicity and co-coercivity, we establish rigorous weak convergence of the generated iterates. Our theoretical analysis integrates tools from operator splitting, inertial acceleration, and monotone operator theory. Extensive experiments on benchmark regression and classification tasks demonstrate that the proposed method achieves faster convergence and higher accuracy than classical and recent forward–backward-type algorithms, delivering consistent state-of-the-art performance.

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Recent publications

Latest Papers

Analysis of LLM Bias (Chinese Propaganda&Anti-US Sentiment) in DeepSeek-R1 vs. ChatGPT o3-mini-high

Jun 02, 2025

This study systematically evaluates the ideological neutrality of large language models (LLMs) within U.S.–China political contexts, comparing China-aligned DeepSeek-R1 and non-China-aligned ChatGPT o3-mini-high on state propaganda and anti-American sentiment. We propose the first cross-lingual (Simplified Chinese, Traditional Chinese, English), decontextualized bias evaluation framework, constructing a 1,200-item multilingual reasoning benchmark. Evaluation combines rubric-guided GPT-4o automated scoring with double-blind human annotation. Results reveal a pronounced “invisible amplifier” effect in DeepSeek-R1: its pro-state and anti-American biases are strongest in Simplified Chinese, attenuate sharply across linguistic shifts (→ Traditional Chinese → English), and generalize beyond politics into cultural domains; ChatGPT o3-mini-high remains largely ideologically neutral. The findings expose a deep coupling between linguistic representation and geopolitical alignment, offering a novel paradigm for assessing value alignment in LLMs.

0 citationsRead paper

A Double Inertial Forward-Backward Splitting Algorithm With Applications to Regression and Classification Problems

May 01, 2025

This paper addresses the problem of finding zeros of the sum of a co-coercive operator and a maximally monotone operator in real Hilbert spaces—a formulation that unifies various regression and classification tasks. To this end, we propose a novel doubly inertial forward–backward splitting algorithm, the first to incorporate two independent, tunable inertia parameters. Crucially, this design accelerates convergence and enhances numerical stability without incurring additional computational cost. Under standard assumptions of monotonicity and co-coercivity, we establish rigorous weak convergence of the generated iterates. Our theoretical analysis integrates tools from operator splitting, inertial acceleration, and monotone operator theory. Extensive experiments on benchmark regression and classification tasks demonstrate that the proposed method achieves faster convergence and higher accuracy than classical and recent forward–backward-type algorithms, delivering consistent state-of-the-art performance.

0 citationsRead paper