Introduces the latest research trends in generative models (SGM) and LLMs presented at NeurIPS 2022.
Introduces BeamCLIP, a technique that efficiently transfers the representational power of the large-scale multimodal model CLIP to a smaller model.
LG AI Research reviewed major research trends in generative models, LLMs, and reinforcement learning presented at NeurIPS 2022.
This piece covers the necessity and principles of Causal Representation Learning, which reasons about causality beyond correlation to overcome the limitations of existing machine learning.
This paper analyzes the limitations of existing machine learning from a Causal Inference perspective and presents the need for causal representation learning.
This introduces PromptRank, a technique that uses LLMs to find efficient document paths for complex Multi-hop Question Answering (MQA).
PromptRank is a technique that combines unsupervised retrieval with an LLM reranker to improve data efficiency in multi-hop question answering.
This introduces the MAMA framework, which maximizes forecasting performance by leveraging multi-resolution information in time series data.
LG AI Research presented the MAMA framework at ICASSP 2023, maximizing time series forecasting performance.
LG AI Research explored the latest vision research trends at CVPR 2023 and discussed the limitations of image captioning through the NICE workshop.
LG AI Research analyzed the computer vision research trends presented at CVPR 2023 and visual reasoning techniques leveraging LLMs.
LG AI Research proposed the QASA benchmark and a multi-step reasoning methodology for deep reasoning on scientific literature.
LG AI Research has released the QASA methodology and benchmark to address complex reasoning in scientific papers.
LG AI Research presented its latest research achievements, including Unlearning and Causal Inference for generative models, at ICML 2023.
LG AI Research unveiled an efficient unlearning technique to protect data privacy in generative models at ICML 2023.
We look at the types of training data and the multifaceted evaluation metrics that determine the performance of LLMs, the core of generative AI.
This provides a detailed analysis of data training, evaluation, and model architecture for Large Language Models (LLMs), the core of generative AI.
LG AI Research's EXAONE model ranked first on KorQuAD 2.0, a benchmark for evaluating Korean machine reading comprehension performance.
LG AI Research achieved the #1 ranking on the Korean machine reading comprehension benchmark KorQuAD 2.0 using EXAONE LM 1.0.
Introducing the latest trends in AI-driven materials database expansion and experimental automation technology presented at the MRS conference.
Google DeepMind expanded the inorganic materials database tenfold through active learning and discovered more than 380,000 new stable materials.
LG AI Research's demand forecasting squad achieved results winning awards in 3 categories at the M6 conference held in New York.
Introduces five major AI research trends and key papers in CV, LLM, and more that drew attention at NeurIPS 2023.
LG AI Research's demand forecasting squad achieved award-winning results in 3 categories at the M6 forecasting competition held in New York.
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