13 citations · 51 across the 35 of their papers we have counts for
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Relation Also Knows: Rethinking the Recall and Editing of Factual Associations in Auto-Regressive Transformer Language Models
Xiyu Liu, Zhengxiao Liu, Naibin Gu +4
The storage and recall of factual associations in auto-regressive transformer language models (LMs) have drawn a great deal of attention, inspiring knowledge editing by directly mo…
Towards Flexible Evaluation for Generative Visual Question Answering
Huishan Ji, Qingyi Si, Zheng Lin +1
Throughout rapid development of multimodal large language models, a crucial ingredient is a fair and accurate evaluation of their multimodal comprehension abilities. Although Visua…
Pruning Large Language Models to Intra-module Low-rank Architecture with Transitional Activations
Bowen Shen, Zheng Lin, Daren Zha +4
Structured pruning fundamentally reduces computational and memory overheads of large language models (LLMs) and offers a feasible solution for end-side LLM deployment. Structurally…
Prediction Exposes Your Face: Black-box Model Inversion via Prediction Alignment
Yufan Liu, Wanqian Zhang, Dayan Wu +3
Model inversion (MI) attack reconstructs the private training data of a target model given its output, posing a significant threat to deep learning models and data privacy. On one…
A Factuality and Diversity Reconciled Decoding Method for Knowledge-Grounded Dialogue Generation
Chenxu Yang, Zheng Lin, Chong Tian +6
Grounding external knowledge can enhance the factuality of responses in dialogue generation. However, excessive emphasis on it might result in the lack of engaging and diverse expr…
Multimodal Table Understanding
Mingyu Zheng, Xinwei Feng, Qingyi Si +4
Although great progress has been made by previous table understanding methods including recent approaches based on large language models (LLMs), they rely heavily on the premise th…
Think out Loud: Emotion Deducing Explanation in Dialogues
Jiangnan Li, Zheng Lin, Lanrui Wang +6
Humans convey emotions through daily dialogues, making emotion understanding a crucial step of affective intelligence. To understand emotions in dialogues, machines are asked to re…
Light-PEFT: Lightening Parameter-Efficient Fine-Tuning via Early Pruning
Naibin Gu, Peng Fu, Xiyu Liu +3
Parameter-efficient fine-tuning (PEFT) has emerged as the predominant technique for fine-tuning in the era of large language models. However, existing PEFT methods still have inade…
Disrupting Diffusion: Token-Level Attention Erasure Attack against Diffusion-based Customization
Yisu Liu, Jinyang An, Wanqian Zhang +4
With the development of diffusion-based customization methods like DreamBooth, individuals now have access to train the models that can generate their personalized images. Despite…
Previously on the Stories: Recap Snippet Identification for Story Reading
Jiangnan Li, Qiujing Wang, Liyan Xu +5
Similar to the "previously-on" scenes in TV shows, recaps can help book reading by recalling the readers' memory about the important elements in previous texts to better understand…
Are Large Language Models Table-based Fact-Checkers?
Hanwen Zhang, Qingyi Si, Peng Fu +2
Table-based Fact Verification (TFV) aims to extract the entailment relation between statements and structured tables. Existing TFV methods based on small-scaled models suffer from…