The ability to predict outcomes from a myriad of parameters has traditionally been anchored in specific, narrowly focused regression methods. While effective within its domain, this specialized approach often needs to be revised when confronted with the complexity and...

The surge of advertisements across online platforms presents a formidable challenge in maintaining content integrity and adherence to advertising policies. While foundational, traditional mechanisms of content moderation grapple with the dual challenges of scale and...

Large Multimodal Models (LMMs), driven by AI advancements, revolutionize vision and language tasks but are mainly centered on English, neglecting non-English languages. This oversight excludes billions of speakers of languages like Chinese, Hindi, Spanish, French,...

Efficiently supporting LLMs is becoming more critical as large language models (LLMs) become widely used. Since getting a new token involves getting all of the LLM’s parameters, speeding up LLM inference is difficult. The hardware is underutilized throughout...

In artificial intelligence, achieving efficiency in neural networks is a paramount challenge for researchers due to its rapid evolution. The quest for methods minimizing computational demands while preserving or enhancing model performance is ongoing. A particularly...

In recent research, the concept of radioactivity in the context of Large Language Models (LLMs) has been discussed,  with particular attention to the detectability of texts created by LLMs. Here, radioactivity refers to the detectable residues left in a model that has...