Ant Group

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China’s stock market operator has pulled the rug from under the largest initial public offering in global finance, calling a halt on the November 5 dual listing of Ant Group’s shares in the city’s technology board less than 48 hours before the highly anticipated trading was due to start.

A meeting earlier this week between Ant Group’s senior executives and China’s top financial regulators constituted “significant change” in the regulatory environment, which may lead to the fintech company not fulfilling the listing requirements or disclosure rules of the exchange, according to a statement by the Shanghai bourse operator.The Shanghai Stock Exchange has decided to temporarily postpone the listing of Ant Group, the market operator said.

Four Chinese regulatory bodies, led by the People’s Bank of China, , on the eve of the fintech company are due to make their trading debut in Shanghai and Hong Kong in the world’s biggest initial public offering.

The regulators met with Ant Group’s co-founder Jack Ma, executive chairman Eric Jing and chief executive Simon Hu, according to a statement by the securities regulator, without elaborating on the purpose or content of the meeting.

The meeting also included representatives from the China Banking and Insurance Regulatory Commission, the China Securities Regulatory Commission and the State Administration of Foreign Exchange (SAFE), the currency regulator.

“On November 2, 2020, Ma as controlling shareholder of Ant Group and Ant’s management team met with Chinese financial regulators,” according to a statement from the Hangzhou-based fintech company. “Views regarding the health and stability of the financial sector were exchanged. Ant Group is committed to implementing the meeting opinions in depth and continuing our course based on the principles of: stable innovation; embrace of regulation; service to the real economy; and win-win cooperation. We will continue to improve our capabilities to provide inclusive services and promote economic development to improve the lives of ordinary citizens.”

- Transaction volumes of inbound travelers using Ant-supported cross-border mobile payment services grew by sixfold year-on-year

- Ant Group adds new international mobile payment partners to facilitate China travel

- American Express and Alipay to deepen partnership

Paper:

Github:

Abstract:

As Large Language Models (LLMs) have made significant advancements across various tasks, such as question answering, translation, text summarization, and dialogue systems, the need for accuracy in information becomes crucial, especially for serious financial products serving billions of users like Alipay. To address this, Alipay has developed a Retrieval-Augmented Generation (RAG) system that grounds LLMs on the most accurate and up-to-date information. However, for a real-world product serving millions of users, the inference speed of LLMs becomes a critical factor compared to a mere experimental model.

Hence, this paper presents a generic framework for accelerating the inference process, resulting in a substantial increase in speed and cost reduction for our RAG system, with lossless generation accuracy. In the traditional inference process, each token is generated sequentially by the LLM, leading to a time consumption proportional to the number of generated tokens. To enhance this process, our framework, named lookahead, introduces a multi-branch strategy. Instead of generating a single token at a time, we propose a Trie-based Retrieval (TR) process that enables the generation of multiple branches simultaneously, each of which is a sequence of tokens. Subsequently, for each branch, a Verification and Accept (VA) process is performed to identify the longest correct sub-sequence as the final output. Our strategy offers two distinct advantages: (1) it guarantees absolute correctness of the output, avoiding any approximation algorithms, and (2) the worstcase performance of our approach is equivalent to the conventional process. We conduct extensive experiments to demonstrate the significant improvements achieved by applying our inference acceleration framework.

Paper:

Github:

Abstract:

As Large Language Models (LLMs) have made significant advancements across various tasks, such as question answering, translation, text summarization, and dialogue systems, the need for accuracy in information becomes crucial, especially for serious financial products serving billions of users like Alipay. To address this, Alipay has developed a Retrieval-Augmented Generation (RAG) system that grounds LLMs on the most accurate and up-to-date information. However, for a real-world product serving millions of users, the inference speed of LLMs becomes a critical factor compared to a mere experimental model.

Hence, this paper presents a generic framework for accelerating the inference process, resulting in a substantial increase in speed and cost reduction for our RAG system, with lossless generation accuracy. In the traditional inference process, each token is generated sequentially by the LLM, leading to a time consumption proportional to the number of generated tokens. To enhance this process, our framework, named lookahead, introduces a multi-branch strategy. Instead of generating a single token at a time, we propose a Trie-based Retrieval (TR) process that enables the generation of multiple branches simultaneously, each of which is a sequence of tokens. Subsequently, for each branch, a Verification and Accept (VA) process is performed to identify the longest correct sub-sequence as the final output. Our strategy offers two distinct advantages: (1) it guarantees absolute correctness of the output, avoiding any approximation algorithms, and (2) the worstcase performance of our approach is equivalent to the conventional process. We conduct extensive experiments to demonstrate the significant improvements achieved by applying our inference acceleration framework.