LlamaParse: An API by LlamaIndex to Efficiently Parse and Represent Files for Efficient Retrieval and Context Augmentation Using LlamaIndex Frameworks
Handling and retrieving information from various file types can be challenging. People often struggle with extracting content from PDFs and spreadsheets, especially when dealing with large volumes. This process can…
Data Complexity and Scaling Laws in Neural Language Models
In Neural Networks, understanding how to optimize performance with a given computational budget is crucial. More processing power devoted to training neural networks usually results in better performance. However, choosing…
Nearest Neighbor Speculative Decoding (NEST): An Inference-Time Revision Method for Language Models to Enhance Factuality and Attribution Using Nearest-Neighbor Speculative Decoding
Large language models (LLMs) have proven their potential to handle multiple tasks and perform extremely well across various applications. However, it is challenging for LLMs to generate accurate information, especially…
Ant Group Proposes MetRag: A Multi-Layered Thoughts Enhanced Retrieval Augmented Generation Framework
The development and application of large language models (LLMs) have experienced significant advancements in Artificial Intelligence (AI). These models have demonstrated exceptional capabilities in understanding and generating human language, impacting…
Scale AI’s SEAL Research Lab Launches Expert-Evaluated and Trustworthy LLM Leaderboards
Scale AI has announced the launch of SEAL Leaderboards, an innovative and expert-driven ranking system for large language models (LLMs). This initiative is a product of the Safety, Evaluations, and…
GNN-RAG: A Novel AI Method for Combining Language Understanding Abilities of LLMs with the Reasoning Abilities of GNNs in a Retrieval-Augmented Generation (RAG) Style
LLMs possess extraordinary natural language understanding capabilities, primarily derived from pretraining on extensive textual data. However, their adaptation to new or domain-specific knowledge is limited and can lead to inaccuracies.…
How RAG helps Transformers to build customizable Large Language Models: A Comprehensive Guide
Natural Language Processing (NLP) has seen transformative advancements over the past few years, largely driven by the developing of sophisticated language models like transformers. Among these advancements, Retrieval-Augmented Generation (RAG)…
RobustRAG: A Unique Defense Framework Developed for Opposing Retrieval Corruption Attacks in Retrieval-Augmented Generation (RAG) Systems
Retrieval-augmented generation (RAG) is a potent strategy that improves the capabilities of Large Language Models (LLMs) by integrating outside knowledge. However, RAG is prone to a particular type of attack…
LLM360 Introduces K2: A Fully-Reproducible Open-Sourced Large Language Model Efficiently Surpassing Llama 2 70B with 35% Less Computational Power
K2 is a cutting-edge large language model (LLM) developed by LLM360 in collaboration with MBZUAI and Petuum. This model, known as K2-65B, boasts 65 billion parameters and is fully reproducible,…
Matryoshka Multimodal Models With Adaptive Visual Tokenization: Enhancing Efficiency and Flexibility in Multimodal Machine Learning
Multimodal machine learning is a cutting-edge research field combining various data types, such as text, images, and audio, to create more comprehensive and accurate models. By integrating these different modalities,…