IBL News | New York
Hallucination in AI, which happens when the model makes stuff up, is the number one roadblock corporations see to adopt LLMs (Large Language Models), according to companies such as Anthropic, Langchain, Elastics, Dropbox, and others.
Reducing and measuring hallucination, along with optimizing contexts, incorporating multimodality, and GPU alternatives, and increasing usability are among the top ten challenges and major research directions today, expert Chip Huyen wrote in an insightful article. Furthermore, many startups are focusing on these problems.
1. Reduce and measure hallucinations
2. Optimize context length and context construction
3. Incorporate other data modalities
4. Make LLMs faster and cheaper
5. Design a new model architecture
6. Develop GPU alternatives
7. Make agents usable
8. Improve learning from human preference
9. Improve the efficiency of the chat interface
10. Build LLMs for non-English languages
• Current English-first LLMs don’t work well for many other languages, both in terms of performance, latency, and speed. Building LLMs for non-English languages opens a new frontier.
Symato might be the biggest community effort today.