• All
  • Articles
  • Paper Skimming
  • Posts
  • Research Highlights
  • Thoughts and Reflections
  • Tutorial

Paper Skimming

Evaluating the Mathematical Reasoning Capabilities of Large Language Models: Limitations and Challenges

Leave a Comment / Paper Skimming / By

LLMs have made remarkable progress in various fields, including natural language processing, question answering, and creative tasks, even demonstrating the ability to solve mathematical problems. Recently, OpenAI’s o1 model, which uses CoT (Chain of Thought), has shown significant reasoning capabilities. However, for a long time, the commonly used GSM8K dataset has had a fixed set of questions […]

Evaluating the Mathematical Reasoning Capabilities of Large Language Models: Limitations and Challenges Read More »

Paper Skimming, , , ,

Introducing Graph RAG: A New Approach to Addressing Global Query Challenges in Large Language Models

Leave a Comment / Paper Skimming / By

Traditional Retrieval-Augmented Generation (RAG) systems often struggle when it comes to handling global queries that require summarizing entire datasets. To address this limitation, a team from Microsoft Research and associated departments has developed a novel method called Graph RAG. This approach combines the strengths of graph-based indexing and query-focused summarization to enhance the ability of

Introducing Graph RAG: A New Approach to Addressing Global Query Challenges in Large Language Models Read More »

Paper Skimming, , , ,

Scaling LLMs for Long Texts with FocusLLM

Leave a Comment / Paper Skimming / By

In traditional Transformer architectures, the computational complexity grows quadratically (O(L²)) with the length of the sequence, making it resource-intensive to process long sequences. This high demand for resources makes it impractical to extend context length directly. Even when fine-tuned on longer sequences, LLMs often struggle with extrapolation, failing to perform well on sequences longer than

Scaling LLMs for Long Texts with FocusLLM Read More »

Paper Skimming, , ,

Enhancing AI with Human-Like Precision: The CriticGPT Approach

Leave a Comment / Paper Skimming / By

Reinforcement Learning from Human Feedback (RLHF) has been fundamental to training models like ChatGPT. However, as models advance, the quality of human feedback reaches a limit, hindering further improvements. OpenAI’s researchers have tackled this challenge by developing CriticGPT—a model trained to detect and correct subtle errors in AI-generated outputs. 🚀 How It Works: 1. Tampering:

Enhancing AI with Human-Like Precision: The CriticGPT Approach Read More »

Paper Skimming, ,
Scroll to Top