Papers by Ninghao Liu
PokeMQA: Programmable knowledge editing for Multi-hop Question Answering (2024.acl-long)
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| Challenge: | Multi-hop question answering (MQA) is one of the challenging tasks to evaluate machine’s comprehension and reasoning abilities, where large language models (LLMs) have widely achieved the human-comparable performance. |
| Approach: | They propose a framework to edit multi-hop question models to update model with up-to-date facts while avoiding expensive re-training or fine-tuning. |
| Outcome: | The proposed framework outperforms all competitors in multi-hop question answering tasks and consistently produces reliable reasoning process. |
Mitigating Shortcuts in Language Models with Soft Label Encoding (2024.lrec-main)
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| Challenge: | Recent studies have shown that large language models rely on spurious correlations in the data for natural language understanding (NLU) tasks. |
| Approach: | They propose a framework for debiasing shortcuts and a dummy class to encode shortcuts into a model and use it to generate soft labels. |
| Outcome: | The proposed framework significantly improves out-of-distribution generalization while maintaining satisfactory in-district accuracy. |
A Survey on Sparse Autoencoders: Interpreting the Internal Mechanisms of Large Language Models (2025.findings-emnlp)
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| Challenge: | Sparse Autoencoders (SAEs) can disentangle complex features into more interpretable components. |
| Approach: | They propose to use Sparse Autoencoders to disentangle LLM features into more interpretable components. |
| Outcome: | The proposed method disentangles complex features into more interpretable components. |
From Language Modeling to Instruction Following: Understanding the Behavior Shift in LLMs after Instruction Tuning (2024.naacl-long)
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| Challenge: | Large Language Models (LLMs) have achieved remarkable success in aligning with user intentions. |
| Approach: | They develop local and global explanation methods and a feed-forward-based method for input-output attribution to investigate the impact of instruction tuning on user intentions. |
| Outcome: | The proposed method compares explanations from pre-trained and instruction-tuned models . it empowers LLMs to recognize the instruction parts of user prompts, it encourages response generation . |
Beyond Input Activations: Identifying Influential Latents by Gradient Sparse Autoencoders (2025.emnlp-main)
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| Challenge: | Sparse Autoencoders (SAEs) have recently emerged as powerful tools for interpreting and steering the internal representations of large language models (LLMs). |
| Approach: | They propose a method that identifies the most influential latents by incorporating output-side gradient information. |
| Outcome: | The proposed method identifies the most influential latents by incorporating output-side gradient information. |
Enhancing Explainable Rating Prediction through Annotated Macro Concepts (2024.acl-long)
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| Challenge: | Existing models learn user and item embeddings and generate reasons based on these embedds. |
| Approach: | They propose a concept-based explanation framework that leverages macro concepts to bridge the gap between the user/item embeddings and the recommendation reasons. |
| Outcome: | Extensive experiments on three datasets prove the proposed model is superior to existing models. |
LMOD: A Large Multimodal Ophthalmology Dataset and Benchmark for Large Vision-Language Models (2025.findings-naacl)
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Zhenyue Qin, Yu Yin, Dylan Campbell, Xuansheng Wu, Ke Zou, Ninghao Liu, Yih Chung Tham, Xiuzhen Zhang, Qingyu Chen
| Challenge: | Existing benchmarks for large vision-language models (LVLMs) are limited to ophthalmology-specific applications. |
| Approach: | They introduce a large-scale multimodal ophthalmology benchmark consisting of 21,993 instances across five ocular imaging modalities and 13 state-of-the-art LVLM representatives from closed-source, open-source and medical domains. |
| Outcome: | The proposed model shows significant performance drop in ophthalmology compared to other domains. |
Denoising Concept Vectors with Sparse Autoencoders for Improved Language Model Steering (2026.findings-eacl)
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| Challenge: | Existing methods for steering concept vectors suffer from noisy features in diverse datasets that undermine steering robustness. |
| Approach: | They propose a Sparse Autoencoder-Denoised Concept Vector (SDCV) which selectively keeps the most discriminative SAE latents while reconstructing hidden representations. |
| Outcome: | The proposed method improves steering success rates by 4-16% across six challenging concepts while maintaining topic relevance. |