Papers by Zhining Liu
Do VLMs Have a Moral Backbone? A Study on the Fragile Morality of Vision-Language Models (2026.findings-acl)
Copied to clipboard
Zhining Liu, Tianyi Wang, Xiao Lin, Penghao Ouyang, Gaotang Li, Ze Yang, Hui Liu, Sumit Keswani, Vishwa Pardeshi, Huijun Zhao, Wei Fan, Hanghang Tong
| Challenge: | Vision-Language Models (VLMs) have advanced multimodal learning, driving progress in cross-modal reasoning. |
| Approach: | They propose to examine moral robustness of vision-language models by analyzing their moral stances under multimodal perturbations. |
| Outcome: | The proposed model-agnostic multimodal perturbations expose VLMs to a variety of moral vulnerabilities, including a sycophancy trade-off where stronger instruction-following models are more susceptible to persuasion. |
AdaFuse: Adaptive Ensemble Decoding for Large Language Models (2026.acl-long)
Copied to clipboard
Chengming Cui, Tianxin Wei, Ziyi Chen, Ruizhong Qiu, Zhichen Zeng, Zhining Liu, Xuying Ning, Duo Zhou, Jingrui He
| Challenge: | Existing ensemble approaches to large language models lack flexibility for mid-generation adaptation. |
| Approach: | They propose an adaptive ensemble decoding framework that dynamically selects semantically appropriate fusion units during generation. |
| Outcome: | The proposed framework outperforms existing ensemble frameworks on open-domain QA, arithmetic reasoning, and machine translation tasks. |
Mem-Gallery: Benchmarking Multimodal Long-Term Conversational Memory for MLLM Agents (2026.acl-long)
Copied to clipboard
Yuanchen Bei, Tianxin Wei, Xuying Ning, Yanjun Zhao, Zhining Liu, Xiao Lin, Yada Zhu, Hendrik Hamann, Jingrui He, Hanghang Tong
| Challenge: | Existing benchmarks evaluate multi-session memory in text-only conversations or assess multimodal understanding within localized contexts. |
| Approach: | They propose a benchmark for evaluating multimodal long-term conversational memory in MLLM agents. |
| Outcome: | The proposed framework assesses key memory capabilities along three functional dimensions: memory extraction and test-time adaptation, memory reasoning, and memory knowledge management. |
Not All Voices Are Rewarded Equally: Probing and Repairing Reward Models across Human Diversity (2025.findings-emnlp)
Copied to clipboard
Zihao Li, Feihao Fang, Xitong Zhang, Jiaru Zou, Zhining Liu, Wei Xiong, Ziwei Wu, Baoyu Jing, Jingrui He
| Challenge: | Using real-world datasets, we conduct the most comprehensive study to date, auditing various state-of-the-art reward models across nine sensitive attributes, including age, gender, ethnicity, etc. |
| Approach: | They propose a method to mitigate group disparities in reward modeling by using real-world data. |
| Outcome: | The proposed method is based on a population-based dataset with nine demographic attributes, including gender, ethnicity, age, gender, and ethnicity. |
SelfElicit: Your Language Model Secretly Knows Where is the Relevant Evidence (2025.acl-long)
Copied to clipboard
| Challenge: | Recent studies have found that Language Models struggle to fully comprehend and utilize key evidence from the context. |
| Approach: | They propose an inference-time approach that helps LMs focus on key contextual evidence through self-guided explicit highlighting. |
| Outcome: | The proposed method improves on multiple evidence-based QA tasks while maintaining computational efficiency. |
Towards Unified Representations of Knowledge Graph and Expert Rules for Machine Learning and Reasoning (2022.aacl-main)
Copied to clipboard
| Challenge: | Empirical study shows superiority of proposed method over time-tested knowledge-driven and data-driven methods. |
| Approach: | They propose a cognitive knowledge graph that unifies expert rules and relational facts as the substrate of machine learning and reasoning models. |
| Outcome: | Empirical results show the proposed method superior to time-tested methods . the proposed model can perform both learning and reasoning with labeled data . |