Papers by Yipeng Kang

4 papers
Simple Role Assignment is Extraordinarily Effective for Safety Alignment (2026.findings-acl)

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Challenge: a new study proposes a role-conditioned pipeline for value alignment . principles alone are incomplete, and they provide little guidance on when and how a value applies in context.
Approach: They propose a role-conditioned pipeline with role-based critics and a model-free approach that is based on role conditioning.
Outcome: The proposed approach outperforms principle-based, Chain-of-Thought and other benchmarks.
Are the Values of LLMs Structurally Aligned with Humans? A Causal Perspective (2025.findings-acl)

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Challenge: Current approaches to value alignment focus on a few core values, such as helpfulness, harmlessness, and honesty.
Approach: They propose to use latent causal value graphs to guide two lightweight value-steering methods . role-based prompting and sparse autoencoder (SAE) steering are also used .
Outcome: Experiments on Gemma-2B-IT and Llama3-8B- IT show that the proposed methods are effective and controllable.
JurisBench: A Deep Benchmark for Assessing Large Language Models in Professional Legal Practice (2026.acl-long)

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Challenge: Existing legal benchmarks evaluate isolated tasks or exam-style questions, failing to capture the procedural interdependencies and adjudicative rigor inherent in professional practice.
Approach: They propose a vertical, depth-oriented, domain-specific benchmark to evaluate Large Language Models (LLMs) in Chinese civil litigation.
Outcome: The proposed benchmarks show that large language models exhibit an "illusion of competence" the results highlight a critical gap between fluent linguistic output and judicial reliability .
Why Are We Moral? An LLM-based Agent Simulation Approach to the Study of Moral Evolution (2026.acl-long)

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Challenge: Existing models of moral evolution must abstract away cognitive processes . et al. (2017): evolution of morality presents a puzzle: natural selection favors selfish .
Approach: They propose an LLM-based agent simulation framework that manipulates cognitive factors to understand moral evolution.
Outcome: The proposed model exploits cognitive realism to explore moral evolution in a hunter-gatherer society.

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