Search Results for author:
Found 10 papers, 6 papers with code
Enhancing Input-Label Mapping in In-Context Learning with Contrastive Decoding
Large language models (LLMs) excel at a range of tasks through in-context learning (ICL), where only a few task examples guide their predictions.
AAKT: Enhancing Knowledge Tracing with Alternate Autoregressive Modeling
In this study, we propose a novel perspective on knowledge tracing task by treating it as a generative process, consistent with the principles of autoregressive models.
Explainable Few-shot Knowledge Tracing
Knowledge tracing (KT), aiming to mine students' mastery of knowledge by their exercise records and predict their performance on future test questions, is a critical task in educational assessment.
Wasserstein Dependent Graph Attention Network for Collaborative Filtering with Uncertainty
Collaborative filtering (CF) is an essential technique in recommender systems that provides personalized recommendations by only leveraging user-item interactions.
ProCQA: A Large-scale Community-based Programming Question Answering Dataset for Code Search
Retrieval-based code question answering seeks to match user queries in natural language to relevant code snippets.
A Review of Data Mining in Personalized Education: Current Trends and Future Prospects
Personalized education, tailored to individual student needs, leverages educational technology and artificial intelligence (AI) in the digital age to enhance learning effectiveness.
Revisiting Demonstration Selection Strategies in In-Context Learning
In this work, we first revisit the factors contributing to this variance from both data and model aspects, and find that the choice of demonstration is both data- and model-dependent.
Towards Making the Most of ChatGPT for Machine Translation
We show that: 1) The performance of ChatGPT depends largely on temperature, and a lower temperature usually can achieve better performance; 2) Emphasizing the task information can further improve ChatGPT's performance, particularly in complex MT tasks; 3) Introducing domain information can elicit ChatGPT's generalization ability and improve its performance in the specific domain; 4) ChatGPT tends to generate hallucinations for non-English-centric MT tasks, which can be partially addressed by our proposed prompts but still need to be highlighted for the MT/NLP community.
Contrastive Learning for Recommender System
To solve this problem, we propose a graph contrastive learning module for a general recommender system that learns the embeddings in a self-supervised manner and reduces the randomness of message dropout.
Similarity Based Auxiliary Classifier for Named Entity Recognition
The segmentation problem is one of the fundamental challenges associated with name entity recognition (NER) tasks that aim to reduce the boundary error when detecting a sequence of entity words.
Ranked #19 on
Named Entity Recognition (NER)
on WNUT 2017
