AI Shorts

Within the field of Natural Language Processing (NLP), resolving references is a critical challenge. It involves identifying the context of specific words or phrases, pivotal to both understanding and successfully managing diverse forms of context. These can range from previous dialogue turns in conversation to non-conversational elements such as user screen entities or background processes. Existing…

Large Language Models (LLMs) are widely used in complex reasoning tasks across various fields. But, their construction and optimization demand considerable computational power, particularly when pretraining on large datasets. To mitigate this, researchers have proposed scaling equations showing the relationship between pretraining loss and computational effort. However, new findings suggest these rules may not thoroughly represent…

Natural Language Processing (NLP) has transformed with the advent of Transformer models. The document generation and summarization, machine translation, and speech recognition abilities of Transformers have exhibited significant progress. Their dominance is specifically seen in large language models (LLMs) that deal with more complex tasks through upscaling transformer architecture. However, the growth of the Transformer…