Official Alimama tech channel, showcasing all of Alimama's technical innovations.
The campus AI Agent competition showcased how large‑language‑model‑powered agents can reason, deceive, and collaborate in a social deduction game, revealing model performance trends, participant insights, and future directions for multi‑agent AI research.
Distribution‑aware Graph Prompt Tuning (DAGPrompT) tackles the pre‑training/downstream mismatch on heterophilic graphs by jointly applying low‑rank GLoRA adaptation and hop‑specific prompts that recast tasks as link‑prediction, yielding up to 4.79% accuracy gains and an average 2.43% improvement in few‑shot node classification.
The paper introduces an explainable LLM framework (ELLM‑rele) that uses chain‑of‑thought reasoning and a multi‑dimensional knowledge distillation pipeline to compress large‑model relevance judgments into lightweight student models, achieving superior offline relevance scores and online click‑through and conversion improvements in Taobao’s search advertising.
The Ali Mama External Investment & Brand Algorithm Series compiles recent KDD, NAACL, and SIGIR research on ad allocation, pacing, traffic estimation, and creative optimization, offering a comprehensive overview of cutting‑edge techniques that enhance advertising efficiency and brand performance across digital platforms.
PosterMaker leverages a ControlNet‑based TextRenderNet with character‑level visual features and a reward‑driven foreground‑extension detector to generate high‑quality product posters that accurately render Chinese text (over 90% sentence accuracy) while preserving product fidelity, and is already deployed in Alibaba’s AI creative tool.
The article details how Apache Paimon, integrated as an external table format in Alibaba’s Dolphin OLAP engine, achieves millisecond‑level query latency and up to 10k QPS through ORC push‑down, manifest conversion, caching, concurrency, and encoding optimizations, outperforming StarRocks and Hologres.
UQABench introduces the first benchmark for assessing high‑density user embeddings that serve as soft prompts in LLM‑driven recommendation, featuring a three‑stage pre‑train‑align‑evaluate pipeline, seven personalized QA tasks, and findings that transformer encoders, side‑information, simple linear adapters, and larger models markedly improve accuracy while cutting input tokens to about five percent.
Alibaba’s Taobao Group unveiled its AIGX technology suite, including the RecGPT recommendation model, the AIGB generative bidding system, and a new AI‑generated video engine, detailing open‑source benchmarks, NeurIPS workshop participation, and measurable ROI improvements for e‑commerce advertising.
In 2024 Alibaba Mama outlines how large‑language models transform search advertising through a three‑line scaling roadmap—explicit inductive‑bias design, implicit compute growth, and auxiliary CV/NLP advances—implemented via a pre‑train/post‑train/CTR paradigm and the LUM user‑behavior model, promising gains in relevance, recall, and real‑time serving while highlighting inference efficiency challenges.
Alibaba Mama’s advertising platform migrated from a monolithic Flink‑Kafka pipeline to a layered Paimon lakehouse, adding DWS upsert support and multi‑layer storage, which delivers minute‑level data freshness, cuts latency by 2.5 hours, reduces resource use over 40 %, halves development effort and achieves ≥99.9 % availability.
