Personalized Large Models: Technical Practice, Challenges, and Future Directions

The article introduces personalized large models as dialogue agents that integrate knowledge, personality, emotion, and memory, distinguishing them from traditional chatbots by offering both productivity and emotional value.

Four core components are outlined: an overview of personalized large models, the role of knowledge, the embodiment of personality, and a concluding outlook.

It explains why personalization is inevitable: large models have surpassed human performance on benchmarks, yet broader scenarios demand stronger interaction and emotional connection. The model’s definition emphasizes a four‑in‑one conversational intelligence.

Research topics span open‑domain knowledge dialogue, empathetic conversation, long‑term memory, and persona‑consistent style, all built on large‑model foundations.

Practical deployments include experiments on Alibaba’s Tmall Genie, role‑play with a comedian, and a gallery of diverse characters on ModelScope, demonstrating the model’s versatility.

The article discusses knowledge hallucination, its causes (data bias, web misinformation, model architecture, sampling), and the heightened risk in open‑domain, long‑turn dialogues. Mitigation strategies include scaling laws, retrieval augmentation, higher‑quality data, and better decoding.

Comparative examples show how integrating a search engine reduces hallucination and improves real‑time factual accuracy.

For personality, four dimensions are identified: consistent persona, distinct language style, logical worldview, and preference‑driven dialogue. Methods to improve persona consistency involve stronger base models, adversarial persona perturbations, and automatic consistency evaluation.

The article surveys personality taxonomies (BigFive, MBTI, and a 638‑trait MIT classification) and proposes using these traits to generate controllable dialogues, supported by experiments measuring style prominence and persona classifier performance.

Finally, the summary emphasizes that enhancing language style requires diverse training data, fine‑grained personality modeling, and robust instruction following beyond simple persona tags.