7 papers
Multi-Persona Thinking for Bias Mitigation in Large Language Models
Yuxing Chen, Guoqing Luo, Zijun Wu +1
Large Language Models (LLMs) exhibit social biases, which can lead to harmful stereotypes and unfair outcomes. We propose \textbf{Multi-Persona Thinking (MPT)}, a simple inference-…
Ultra-Low-Dimensional Prompt Tuning via Random Projection
Zijun Wu, Yongchang Hao, Lili Mou
Large language models achieve state-of-the-art performance but are increasingly costly to fine-tune. Prompt tuning is a parameter-efficient fine-tuning method that addresses parame…
TokMem: One-Token Procedural Memory for Large Language Models
Zijun Wu, Yongchang Hao, Lili Mou
Large language models are typically controlled via prompts, which must be repeatedly re-processed for every new query and are difficult to reuse modularly. We introduce TokMem, a p…
The Emergence of Chunking Structures with Hierarchical RNN
Zijun Wu, Anup Anand Deshmukh, Yongkang Wu +2
In Natural Language Processing (NLP), predicting linguistic structures, such as parsing and chunking, has mostly relied on manual annotations of syntactic structures. This paper in…
MonkeyOCR v1.5 Technical Report: Unlocking Robust Document Parsing for Complex Patterns
Jiarui Zhang, Yuliang Liu, Zijun Wu +17
Document parsing is a core task in document intelligence, supporting applications such as information extraction, retrieval-augmented generation, and automated document analysis. H…
Cross-Modal Consistency in Multimodal Large Language Models
Xiang Zhang, Senyu Li, Ning Shi +5
Recent developments in multimodal methodologies have marked the beginning of an exciting era for models adept at processing diverse data types, encompassing text, audio, and visual…