Abstract: With the ever-increasing number of news stories available online, classifying them by topic, regardless of the language they are written in, has become crucial for enhancing readers’ access ...

In case you use any of the components for your research, please refer to (and cite) this paper: "LLM Teacher-Student Framework for Text Classification With No Manually Annotated Data: A Case Study in ...

Smaller doesn't mean lesser ...

NLP and LLM teams often grow their training corpuses to improve model performance but they still do not always obtain ...

Abstract: With large-scale language models demonstrating superior capabilities in a wide range of downstream natural language processing tasks, the future trajectory of research in the field of text ...

Bigger has defined AI from day one. New data says task-specific small models beat frontier LLMs on accuracy, cost and speed — ...

Before diving into the internals of an LLM, it’s a good idea to first understand what an LLM actually is. In this blog, we will learn about BPE (Byte Pair Encoding) - the tokenization algorithm used ...

Token minimizing is the fastest way to lower LLM costs and latency. Learn practical techniques: prompt trimming, compaction, ...

Overview:  Large language models may dominate headlines, but modern NLP tools remain essential for text processing, ...

Speech recognition accuracy benchmarks report low error rates while leaving the most critical words wrong. Researchers now ...

Katha Room hosts more than 250 stories across five languages and has notched over 10,000 downloads on iOS and Android combined, while being bootstrapped. Katha Room addresses the decline of ...

CLIP is a seminal multimodal model that maps images and text into a shared representation space by contrastive learning on billions of image–caption pairs. Inspired by the rapid progress of large ...