EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Hacker News·Jul 24·5 min read
LLMs Are Still Toxic, Stuck in the Past, and Bad at Math
TL;DR
Despite nearly four years since the release of ChatGPT, major issues like toxicity, hallucinations, and poor math skills persist in large language models (LLMs). While recent tools have improved usability, the underlying problems remain largely unchanged.
✦ Why It Matters
Evaluate your current use of LLMs and consider implementing additional safety measures to mitigate toxicity.
Key Takeaways
- Use external tools for complex math calculations with LLMs.
- Be aware of LLMs' knowledge cutoff dates when seeking current information.
- Implement reinforcement learning techniques to improve model alignment.
- Understand the limitations of LLM memory and context handling.
How It Works
LLMs like ChatGPT utilize a harness to manage tool calls for complex tasks. When faced with difficult math problems, the model can trigger an external Python script to compute the answer, effectively bypassing its inherent limitations.
⚠ The Catch
LLMs are stateless and require the entire conversation transcript for context, leading to inefficiencies and limitations in memory handling.