The advancement of artificial intelligence (AI) and machine learning (ML) has enabled transformative progress across diverse fields. However, the “system domain,” which focuses on optimizing and ...

Tokenization, the process of breaking text into smaller units, has long been a fundamental step in natural language processing (NLP). However, it presents several challenges. Tokenizer-based language ...

It can significantly enhance LLMs’ problem-solving capabilities by guiding them to think more deeply about complex problems and effectively utilize inference-time computation. Prior research has ...

The design and deployment of modern RLMs pose a lot of challenges. They are expensive to develop, have proprietary restrictions, and have complex architectures that limit their access. Moreover, the ...

Out of the various methods employed in document search systems, “retrieve and rank” has gained quite some popularity. Using this method, the results of a retrieval model are re-ordered according to a ...

Lexicon-based embeddings are one of the good alternatives to dense embeddings, yet they face numerous challenges that restrain their wider adoption. One key problem is tokenization redundancy, whereby ...

Aligning large language models (LLMs) with human values is essential as these models become central to various societal functions. A significant challenge arises when model parameters cannot be ...

Modern NLP applications often demand multi-step reasoning, interaction with external tools, and the ability to adapt dynamically to user queries. Haystack Agents, an innovative feature of the Haystack ...

Smartphones are essential tools in dAIly life. However, the complexity of tasks on mobile devices often leads to frustration and inefficiency. Navigating applications and managing multi-step processes ...

The study of autonomous agents powered by large language models (LLMs) has shown great promise in enhancing human productivity. These agents are designed to assist in various tasks such as coding, ...

Now, let’s look into their latest research on ZKLoRA. In this research, the Bagel Research Team focuses on enabling efficient and secure verification of Low-Rank Adaptation (LoRA) updates for LLMs in ...

Sequences are a universal abstraction for representing and processing information, making sequence modeling central to modern deep learning. By framing computational tasks as transformations between ...