Key Technologies for Domain‑Specific Large Models: Insights from the World AI Conference

The article summarizes Professor Xiao Yanghua’s talk at the World AI Conference, focusing on the essential technologies for applying large language models (LLMs) to specific domains.

It argues that vertical domains still require general LLMs because their open‑world understanding provides a foundational knowledge base that can be further refined with domain data, mirroring how human education builds from general to specialized knowledge.

Key capabilities of LLMs highlighted include:

Open‑world understanding, enabling broad comprehension of diverse natural‑language queries.

Combinatorial innovation, allowing the model to blend skills such as poetry generation and code annotation.

Evaluation ability, where large models can act as expert judges for tasks like translation quality assessment.

Complex instruction comprehension and execution, producing faithful outputs when given detailed prompts.

Task decomposition and planning, breaking down intricate problems into manageable steps and coordinating with traditional IT systems.

Symbolic reasoning, covering common‑sense, logical, and numerical inference.

Despite these strengths, the report notes several limitations: current LLMs cannot directly handle many complex, high‑stakes vertical tasks, suffer from hallucinations, lack fidelity to provided documents, and may produce overly generic or contradictory answers.

To bridge the gap, several optimization directions are proposed:

Enhancing long‑text comprehension (e.g., extending context windows beyond 2‑4K tokens).

Improving planning and tool‑collaboration capabilities so models can decide when to invoke external knowledge bases or APIs.

Advancing structured output and style control to meet domain‑specific formatting requirements.

Ensuring answer fidelity and consistent belief alignment, avoiding both evasive and overly stubborn responses.

Strengthening data governance, including data cleaning, privacy detection, bias mitigation, and compliance certification.

Developing evaluation frameworks that balance high‑score performance with real‑world problem‑solving ability.

In conclusion, while general LLMs provide a powerful foundation, substantial research on model scaling, domain‑specific fine‑tuning, tool integration, and rigorous data governance is essential for them to become reliable engines for vertical industry applications and to drive high‑quality digital transformation.