2027 International Conference on Language Intelligence and Large Language Models (LI‑LLM 2027)

We sincerely invite global scholars, senior researchers, AI engineers, industry technical leaders and policy researchers to join the 2027 International Conference on Language Intelligence and Large Language Models (LI‑LLM 2027) in Shanghai, China, during May 14–16, 2027.

Driven by the explosive evolution of natural language processing, foundation models, multimodal AI and cognitive computing, large language models (LLMs) have reconstructed human‑computer interaction, academic research, industrial manufacturing and public service sectors. Over recent years, LLMs have evolved from experimental academic prototypes into core digital infrastructure that fuels global digital‑intelligent transformation, bringing disruptive innovation across education, healthcare, finance, law and many other vertical domains. Despite remarkable practical progress, theoretical understanding still lags behind fast‑growing real‑world deployment, creating an urgent demand for dedicated international venues to align academic exploration with industrial practice. Nevertheless, the community still faces urgent open challenges: model hallucinations, unstable reasoning, data privacy risks, safety alignment, low‑resource language adaptation, lightweight edge deployment, bias and interpretability defects.

Against the backdrop of booming large‑model innovation and rising cross‑border demands for trustworthy AI governance, this conference is established to fill the gap for focused, in‑depth communication covering theoretical advances, engineering optimization and regulatory practice for language‑centric foundation models. The conference unites top‑tier university scholars, enterprise R&D experts and cross‑disciplinary practitioners to share the latest theoretical breakthroughs, optimized LLM architectures, trustworthy governance frameworks and scalable industrial landing solutions. Our core mission is to bridge the gap between fundamental theoretical research and real‑world commercial applications, explore safe, interpretable and sustainable next‑generation foundation models, and foster long‑term cross‑border academic collaboration.