Structure & Knowledge Injection into Machine Learning
Embedded Files
Recent News
Recent News
01/23/25 1 paper accepted at ICLR 2025!
01/13/25 1 intern has joined SK-ML.
12/01/24 8 graduate students have joined SKI-ML.
11/29/24 1 paper accepted at COLING 2025 (Oral) !
09/20/24 4 papers accepted at EMNLP 2024 (3 EMNLP, 1 TACL) !
08/15/24 Our member (Jonghyun Song) presented a spotlight talk at the ACL Workshop on Representation Learning for NLP (Repl4NLP).
08/01/24 3 papers accepted at ACL 2024 workshop!
06/01/24 4 graduate students have joined SKI-ML.
12/10/23 Jay-Yoon Won the Best Area Chair award for the "Machine Learning for NLP track" at EMNLP 2023!
12/06/23 Our paper "Machine Reading Comprehension using Case-based Reasoning" got accepted at EMNLP 2023!
Research Topics
Research Topics
NLP Developing AI models that use large language models to enhance their ability to perform complex reasoning tasks.
NLP Developing AI models that use large language models to enhance their ability to perform complex reasoning tasks.
Knowledge Injection Focusing on the integration of human knowledge into machine learning models to enhance learning with constraints.
Knowledge Injection Focusing on the integration of human knowledge into machine learning models to enhance learning with constraints.
Structured Prediction Leveraging tools such as Energy-Based Models (EBM) to predict complex structured data.
Structured Prediction Leveraging tools such as Energy-Based Models (EBM) to predict complex structured data.
SKI-ML (Structure and Knowledge Injection into Machine Learning) aims to enhance machine learning capabilities by incorporating structured prediction and human intelligence into AI models. Our lab specializes in the strategic use of advanced machine learning techniques such as Energy-Based Models (EBM) to process and predict structured data efficiently. We engage in various NLP tasks using large language models (LLM), with a particular emphasis on improving their reasoning abilities. This integrative approach not only boosts the adaptability and precision of AI systems but also deepens the synergy between human knowledge and artificial intelligence, enabling our technologies to excel in diverse real-world applications. Our work aims to bridge theoretical insights and practical implementations, propelling a deeper connection and understanding between the realms of human cognition and machine learning.
SKI-ML (Structure and Knowledge Injection into Machine Learning) aims to enhance machine learning capabilities by incorporating structured prediction and human intelligence into AI models. Our lab specializes in the strategic use of advanced machine learning techniques such as Energy-Based Models (EBM) to process and predict structured data efficiently. We engage in various NLP tasks using large language models (LLM), with a particular emphasis on improving their reasoning abilities. This integrative approach not only boosts the adaptability and precision of AI systems but also deepens the synergy between human knowledge and artificial intelligence, enabling our technologies to excel in diverse real-world applications. Our work aims to bridge theoretical insights and practical implementations, propelling a deeper connection and understanding between the realms of human cognition and machine learning.
Page updated
Google Sites
Report abuse