Papers by Kang Zhong

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

Copied to clipboard

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.
Aligning VLM Assistants with Personalized Situated Cognition (2025.acl-long)

Copied to clipboard

Challenge: Existing studies on vision-language models aligned with general human objectives have not been successful because people with diversified backgrounds have different cognition even in the same situation.
Approach: They propose to characterize individuals based on the sociological concept of Role-Set and then evaluate their actions to see whether personalized alignment is achieved.
Outcome: The proposed framework constructs a cognition-aware and action-based reward model for personalized alignment.
P²Net: Parallel Pointer-based Network for Key Information Extraction with Complex Layouts (2025.findings-acl)

Copied to clipboard

Challenge: Existing methods for key information extraction are based on a limited set of entity categories and fixed layouts.
Approach: They propose a large-scale, human-annotated dataset for key information extraction . it is based on a human-annotated layout and 1,162 entity categories . they propose 'parallel pointer-based network' that leverages implicit relationships .
Outcome: Experiments on widely-used datasets show that the proposed model outperforms state-of-the-art methods while maintaining fast inference speeds.
Are the Values of LLMs Structurally Aligned with Humans? A Causal Perspective (2025.findings-acl)

Copied to clipboard

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.
When Phrases Meet Probabilities: Enabling Open Relation Extraction with Cooperating Large Language Models (2024.acl-long)

Copied to clipboard

Challenge: Existing clustering-based open relation extraction methods use pre-trained language models . embeddings from language models are high-dimensional and anisotropic, so there is a gap .
Approach: They propose a framework that makes two LLMs work collaboratively to achieve clustering.
Outcome: The proposed framework outperforms existing methods by 1.4%3.13% on different datasets.
Why Are We Moral? An LLM-based Agent Simulation Approach to the Study of Moral Evolution (2026.acl-long)

Copied to clipboard

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.
From Past To Path: Masked History Learning for Next-Item Prediction in Generative Recommendation (2026.acl-long)

Copied to clipboard

Challenge: Generative recommendation models inherently bias towards local contexts, failing to capture deeper historical dependencies necessary for understanding complex user intents.
Approach: They propose a training framework that shifts the objective from simple next-step prediction to deep comprehension of history by entropy-guided masking policy and a curriculum learning scheduler to enhance the framework.
Outcome: The proposed framework outperforms state-of-the-art generative models on three public datasets and shows that it is more accurate than current models.
Enhancing Chinese Offensive Language Detection with Homophonic Perturbation (2025.emnlp-main)

Copied to clipboard

Challenge: Detecting offensive language in Chinese is challenging due to homophonic substitutions used to evade detection.
Approach: They propose to use HED-COLD to build a large-scale homophonic dataset for Chinese offensive language detection and a homophone-aware pretraining strategy to learn phonetics and orthography.
Outcome: The proposed framework achieves state-of-the-art performance on the COLD test set and the toxicity benchmark ToxiCloakCN.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations