Unlocking Vertical Domain LLMs: Advantages, Challenges, and Alignment Strategies

Vertical Domain Large Models

Vertical domain large models are built on a general base model and are further trained with domain‑specific knowledge, resulting in higher expertise and practicality for targeted industries. Compared with generic models, they offer stronger domain specialization but also face distinct challenges.

Advantages

Domain expertise : Specialized training enables better understanding of industry terminology and context.

High‑quality output : Optimization for a specific field yields more accurate results.

Better task performance : For domain‑specific tasks, vertical models often outperform generic ones.

Challenges

Accuracy : Business users demand higher precision; trial‑and‑error costs are high.

Knowledge‑base maintenance : Frequent updates and diverse formats (flowcharts, PDFs, PPTs) make reliable extraction and retrieval difficult.

Applicability limits : Strong performance in one domain may not transfer to others, requiring mixed fine‑tuning data.

Alignment Enhancement (BPO)

We adopt Black‑Box Prompt Optimization (BPO) to improve question understanding and answer quality.

Step1: Provide a large model A with an init instruction and let it generate answers A' for standard Q‑A pairs, creating triples (Q, A, A').

Step2: Use GPT‑4 to compare good and bad answers with the question and refine the init instruction into a tuned instruction.

Step3: Train a seq2seq model that maps a question Q to the tuned instruction.

Step4: Deploy the seq2seq model so that every user query is first transformed into an optimized prompt, then fed to the large model for answering.

Applying BPO increased answer accuracy by 1.8%.

Example

Original question: "After work, recommend a good restaurant nearby?"

Optimized prompt after alignment: "It's Friday, traffic is heavy, I want something spicy, recommend a restaurant within a 10‑minute drive."

Text2API

We treat the LLM as an agent that learns to invoke over 1,000 high‑frequency APIs. Challenges include hallucinated parameters with Chinese models and long LangChain call chains. Switching to the Reflexion framework, which adds self‑reflection and memory, improved API accuracy by 4%.

Retrieval‑Augmented Generation (RAG)

RAG combines domain‑specific databases with LLM generation. Complex materials like flowcharts, tables, and screenshots require robust parsing. We first let ChatGPT describe flowchart steps, manually review ~1,000 results, and incorporate them into the base model via SFT + DPO.

For text chunking, we balance chunk size to retain information while fitting context windows, re‑cluster chunks by semantic logic, and recursively summarize until the desired length is reached, forming a hierarchical tree of information.

SFT and Preference Optimization

We collected tens of thousands of annotated evaluation scenarios to guide base model selection and fine‑tuning. Using embedding similarity, human scoring, and GPT‑4 scoring, we benchmarked open‑source models and fine‑tuning methods. Public datasets (COIG‑CQIA, alpaca‑gpt4‑data‑cn) were mixed in to mitigate domain‑specific capability degradation.

Adopting ORPO (preference optimization without a reference model) within SFT added a penalty term, yielding a 5.2% overall answer quality improvement.

Conclusion

Our team’s year‑long exploration of vertical domain LLMs has achieved notable breakthroughs, yet many challenges remain. We invite the community to collaborate and advance large‑model technologies together.