Robuta

Retrieval of information from graph-structured knowledge bases represents a promising direction for improving the factuality of LLMs. While various solutions...

Advancements in Large Language Models (LLMs) have enabled developers to create a variety of applications such as code generation, translation, and text sum

One main approach to reducing the massive costs of large language models (LLMs) is the use of quantized or sparse representations for training or deployment....

Sonarweep validates quality coding datasets, deduplicating, fixing issues & filtering noise to help your LLM train efficiently and produce more secure,...

Shivprasad Rajendra Sagare, Hemachandran S, Kinshuk Sarabhai, Prashant Ullegaddi, Rajeshkumar Sa. Proceedings of the 17th International Natural Language...

Abstract page for arXiv paper 2506.10364: Can We Infer Confidential Properties of Training Data from LLMs?

Jens Van Nooten, Andriy Kosar. Proceedings of the Joint Workshop of the 7th Financial Technology and Natural Language Processing, the 5th Knowledge Discovery...

We propose **DiFF**PO, **Di**ffusion **F**ast and **F**urious Policy Optimization, a unified framework for training masked diffusion large language models...

Apply fine-tuning and reinforcement learning techniques to shape model behavior, improve reasoning, and make LLMs safer and more reliable.

The immense model sizes of large language models (LLMs) challenge deployment on memory-limited consumer GPUs. Although model compression and parameter...