Balancing the Brains: Tackling the Composition-Knowledge Challenge in LLMs
LLMs often struggle with integrating compositionality and knowledge. New methods like Concretized Proposition Prompting show promise in bridging this gap.
Read the full articleYou might also wanna read
From Prompt Engineering to Context Engineering: Evolving LLM Inference Approaches
As our use of LLMs has changed from conversational chatbots and into integral decision-making components of complex systems, our inference a
chrisloy.dev·8mo ago
A Journey from AI to LLMs and MCP - 3 - Boosting LLM Performance – Fine-Tuning, Prompt Engineering, and RAG
## Free Resources - **[Free Apache Iceberg Course](
Enhancing Abstraction in Large Language Models Through Nature-Inspired Semantic Patterns
Recent advancements in artificial intelligence emphasize improving the abstraction capabilities of Large Language Models (LLMs) by integrati
ieeexplore.ieee.org·1y ago
Comprehensive Survey of Reasoning Failures in Large Language Models
Large Language Models (LLMs) have exhibited remarkable reasoning capabilities, achieving impressive results across a wide range of tasks. De
arxiv.org·4mo agoA Journey from AI to LLMs and MCP - 3 - Boosting LLM Performance – Fine-Tuning, Prompt Engineering, and RAG
> **Cross-posted.** This article's canonical home is [Data Lakehouse Hub]( ## Free Res...
Research Reveals Reasoning LLMs Lack Systematic Problem-Solving Capabilities
Large Language Models (LLMs) have demonstrated impressive reasoning abilities through test-time computation (TTC) techniques such as chain-o
arxiv.org·9mo ago
Comments
Sign in to join the conversation.
No comments yet. Be the first.