LLMs are not experts which often make mistakes though they are good at knowledge. It is expected that LLMs to have at least 20-30% of errors, ep. in the vertical domain. The reason is because the corpus used for training the LLMs is not perfect.
It is believed that LLMs may be good at 2D layout reasoning, but weak at 3D.
LLMs may be not good at logic and logical reasoning.
A long way to go towards fine-grained visual understanding.
2. Use LLMs for information seeking: key research directions
LLM-based search and recommendation
Integration of generation and retrieval
Proactive dialog
Trust
Safety
Evaluation of LLMs
Others
3. Summarization: Research beyond LLMs
LLM will disrupt research and commercial activities in many aspects.
A lot of research can still be done at academia: (1) Understand the fundamentals in LLM (subset that understand human language and common sense knowledge; general limitations & weaknesses of LLMs)· (2) Trust in LLM. (3) Audit in LLM· (4) Network of experts vs. a generalist Decision with Human-in-the-Loop. (5) Others: Large Foundation Models for multimodality and life science etc.
In every decade, there are disruptions to technologies and our ways of doing things: (1) With it creates new opportunities; (2) Opportunities from LLMs: easier to do many things; making many new applications possible; improve performance; (3) With it new research directions and business opportunities.
Only has compared to the others early, diligently, can feel the successful taste.