Large-scale multimodal foundation models have achieved notable success in understanding complex visual patterns and natural language, generating interest in their application to medical vision-language tasks. Progress has been made by creating medical datasets with image-text pairs and fine-tuning general domain models on these datasets. However, these datasets have limitations. They lack multi-granular annotations that link… →

The ability to convert natural language questions into structured query language (SQL), known as text-to-SQL, helps non-experts easily interact with databases using natural language. This makes data access and analysis more accessible to everyone. Recent studies have highlighted significant achievements in powerful closed-source large language models (LLMs) like GPT-4, which use advanced prompting techniques. However,… →

As LLMs have become increasingly capable of performing various tasks through few-shot learning and instruction following, their inconsistent output formats have hindered their reliability and usability in industrial contexts. This inconsistency complicates the extraction and evaluation of generated content, particularly when structured generation methods, such as JSON and XML, are employed. The authors investigate whether… →

Over 300,000 photos in earlier massive datasets like COCO have over 3 million annotations. Models may now be trained on datasets with a 1000x increase in scale, such as FLD-5B, which contains over 126 million photos annotated with five billion+ words. Annotation speed can be increased by a factor of 100 with synthetic annotation pipelines,… →

Natural Language Processing (NLP), despite its progress, faces the persistent challenge of hallucination, where models generate incorrect or nonsensical information. Researchers have introduced Retrieval-Augmented Generation (RAG) systems to mitigate this issue by incorporating external information retrieval to enhance the accuracy of generated responses. The problem, however, is the reliability and effectiveness of RAG systems in… →

LG AI Research has recently announced the release of EXAONE 3.0. This latest third version in the series upgrades EXAONE’s already impressive capabilities. The release as an open-source large language model is unique to the current version with great results and 7.8B parameters. With the introduction of EXAONE 3.0, LG AI Research is driving a… →

Calendars, specifically Google Calendars, have both positive and negative aspects. For example, they can help plan gatherings, track time spent on individual tasks, and even keep in touch with pals. However, our schedule has the potential to balloon out of control quickly. Having nothing to go on but a sea of blue checkboxes on your… →

Large Language Models (LLMs) are advancing rapidly resulting in more complex architecture. The high cost of LLMs has been a major barrier to their widespread adoption in various industries. Businesses and developers have been hesitant to invest in these models due to the substantial operational expenses. A significant portion of these costs arises from the… →

Visual representation learning using large models and self-supervised techniques has shown remarkable success in various visual tasks. However, deploying these models in real-world applications is challenging due to multiple resource constraints such as computation, storage, and power consumption. Adapting large pre-trained models for different scenarios with varying resource limitations involves weight pruning, knowledge distillation, or… →

Recent advances in segmentation foundation models like the Segment Anything Model (SAM) have shown impressive performance on natural images and videos. Still, their application to medical data remains to be determined. SAM, trained on a vast dataset of natural images, struggles with medical images due to domain differences like lower resolution and unique image challenges.… →