10 citations · 22 across the 12 of their papers we have counts for
13 papers
REAL: Reading Out Transformer Activations for Precise Localization in Language Model Steering
Li-Ming Zhan, Bo Liu, Chengqiang Xie +2
Inference-time steering aims to alter a large language model's (LLM's) responses without changing its parameters, but a central challenge is identifying the internal modules that m…
GEMeX: A Large-Scale, Groundable, and Explainable Medical VQA Benchmark for Chest X-ray Diagnosis
Bo Liu, Ke Zou, Liming Zhan +7
Medical Visual Question Answering (Med-VQA) combines computer vision and natural language processing to automatically answer clinical inquiries about medical images. However, curre…
Diversity-grounded Channel Prototypical Learning for Out-of-Distribution Intent Detection
Bo Liu, Liming Zhan, Yujie Feng +5
In the realm of task-oriented dialogue systems, a robust intent detection mechanism must effectively handle malformed utterances encountered in real-world scenarios. This study pre…
TaSL: Continual Dialog State Tracking via Task Skill Localization and Consolidation
Yujie Feng, Xu Chu, Yongxin Xu +3
A practical dialogue system requires the capacity for ongoing skill acquisition and adaptability to new tasks while preserving prior knowledge. However, current methods for Continu…
Continual Dialogue State Tracking via Reason-of-Select Distillation
Yujie Feng, Bo Liu, Xiaoyu Dong +4
An ideal dialogue system requires continuous skill acquisition and adaptation to new tasks while retaining prior knowledge. Dialogue State Tracking (DST), vital in these systems, o…
KIF: Knowledge Identification and Fusion for Language Model Continual Learning
Yujie Feng, Xu Chu, Yongxin Xu +4
Language model continual learning (CL) has recently attracted significant interest for its ability to adapt large language models (LLMs) to dynamic real-world scenarios without ret…
VI-OOD: A Unified Representation Learning Framework for Textual Out-of-distribution Detection
Li-Ming Zhan, Bo Liu, Xiao-Ming Wu
Out-of-distribution (OOD) detection plays a crucial role in ensuring the safety and reliability of deep neural networks in various applications. While there has been a growing focu…
Towards LLM-driven Dialogue State Tracking
Yujie Feng, Zexin Lu, Bo Liu +2
Dialogue State Tracking (DST) is of paramount importance in ensuring accurate tracking of user goals and system actions within task-oriented dialogue systems. The emergence of larg…
EasyGen: Easing Multimodal Generation with BiDiffuser and LLMs
Xiangyu Zhao, Bo Liu, Qijiong Liu +2
We present EasyGen, an efficient model designed to enhance multimodal understanding and generation by harnessing the capabilities of diffusion models and large language models (LLM…
How Good Are LLMs at Out-of-Distribution Detection?
Bo Liu, Liming Zhan, Zexin Lu +3
Out-of-distribution (OOD) detection plays a vital role in enhancing the reliability of machine learning (ML) models. The emergence of large language models (LLMs) has catalyzed a p…
Multi-modal Pre-training for Medical Vision-language Understanding and Generation: An Empirical Study with A New Benchmark
Li Xu, Bo Liu, Ameer Hamza Khan +2
With the availability of large-scale, comprehensive, and general-purpose vision-language (VL) datasets such as MSCOCO, vision-language pre-training (VLP) has become an active area…
Out-of-Scope Intent Detection with Self-Supervision and Discriminative Training
Li-Ming Zhan, Haowen Liang, Bo Liu +3
Out-of-scope intent detection is of practical importance in task-oriented dialogue systems. Since the distribution of outlier utterances is arbitrary and unknown in the training st…
SLAKE: A Semantically-Labeled Knowledge-Enhanced Dataset for Medical Visual Question Answering
Bo Liu, Li-Ming Zhan, Li Xu +3
Medical visual question answering (Med-VQA) has tremendous potential in healthcare. However, the development of this technology is hindered by the lacking of publicly-available and…