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GLM introduces a unified pretraining framework that combines autoregressive blank‑filling with 2D positional encoding and span‑shuffle, achieving superior performance over BERT, T5 and GPT on a range of NLU and generation tasks such as SuperGLUE, text‑filling, and language modeling.
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ChatGPT, a dialogue-focused language model, builds on the GPT family and employs techniques such as Reinforcement Learning from Human Feedback (RLHF), the TAMER framework, and a three-stage training pipeline (supervised fine‑tuning, reward modeling, and PPO reinforcement learning) to achieve advanced conversational capabilities.
Prof. Qiu Xipeng’s talk highlighted how large language models can be offered as a service and efficiently adapted via in‑context learning, lightweight label‑tuning, and gradient‑free black‑box optimization, showcasing a unified asymmetric Transformer (CPT) that handles understanding, generation, ABSA and NER tasks while reducing resource demands.
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This article explains the role of Weighted Finite State Transducers in conventional HMM‑based speech recognition, covering language models, pronunciation dictionaries, WFST definitions, semiring theory, composition and determinization operations, decoding graph construction (HCLG), lattice rescoring, and practical optimization techniques for real‑world scenarios.
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DiDi’s self‑learning ASR platform lets non‑technical users upload business data, automatically train, test and deploy models with semi‑supervised learning, hot‑word updates and LSTM rescoring, creating a closed‑loop pipeline that boosted vehicle voice‑interaction accuracy from around 80 % to over 95 % within months.
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Intelligent speech recognition converts spoken audio to text using a pipeline of feature extraction, acoustic and language modeling, where deep neural networks—especially CNN, LSTM, and hybrid CLDNN architectures—drive high accuracy, enabling mobile voice input, call‑center transcription, legal record keeping, and Tencent Cloud ASR’s 97% Mandarin accuracy with speaker separation and on‑premises deployment.
The article outlines the design and implementation of a multi‑turn conversational chatbot, detailing how natural‑language understanding converts user utterances into structured representations, a CNN‑LSTM language model classifies topics, intents, and sentiments, and an XML‑based answer engine orchestrates tasks and services for real‑world deployment.