10 citations · 32 across the 8 of their papers we have counts for
8 papers
Selecting Demonstrations for Many-Shot In-Context Learning via Gradient Matching
Jianfei Zhang, Bei Li, Jun Bai +4
In-Context Learning (ICL) empowers Large Language Models (LLMs) for rapid task adaptation without Fine-Tuning (FT), but its reliance on demonstration selection remains a critical c…
AAKT: Enhancing Knowledge Tracing with Alternate Autoregressive Modeling
Hao Zhou, Wenge Rong, Jianfei Zhang +3
Knowledge Tracing (KT) aims to predict students' future performances based on their former exercises and additional information in educational settings. KT has received significant…
Disentangling Preference Representation and Text Generation for Efficient Individual Preference Alignment
Jianfei Zhang, Jun Bai, Bei Li +4
Aligning Large Language Models (LLMs) with general human preferences has been proved crucial in improving the interaction quality between LLMs and human. However, human values are…
Leveraging Estimated Transferability Over Human Intuition for Model Selection in Text Ranking
Jun Bai, Zhuofan Chen, Zhenzi Li +5
Text ranking has witnessed significant advancements, attributed to the utilization of dual-encoder enhanced by Pre-trained Language Models (PLMs). Given the proliferation of availa…
ProCQA: A Large-scale Community-based Programming Question Answering Dataset for Code Search
Zehan Li, Jianfei Zhang, Chuantao Yin +2
Retrieval-based code question answering seeks to match user queries in natural language to relevant code snippets. Previous approaches typically rely on pretraining models using cr…
Improving Variational Autoencoders with Density Gap-based Regularization
Jianfei Zhang, Jun Bai, Chenghua Lin +2
Variational autoencoders (VAEs) are one of the powerful unsupervised learning frameworks in NLP for latent representation learning and latent-directed generation. The classic optim…
Enhancing Dual-Encoders with Question and Answer Cross-Embeddings for Answer Retrieval
Yanmeng Wang, Jun Bai, Ye Wang +5
Dual-Encoders is a promising mechanism for answer retrieval in question answering (QA) systems. Currently most conventional Dual-Encoders learn the semantic representations of ques…
Deep calibration of the quadratic rough Heston model
Mathieu Rosenbaum, Jianfei Zhang
The quadratic rough Heston model provides a natural way to encode Zumbach effect in the rough volatility paradigm. We apply multi-factor approximation and use deep learning methods…