Autoregressive Generation

The self-attention mechanism allows the model to simultaneously reference the contextual information of all other tokens in a sequence when processing each token, rather than processing sequentially as RNNs do

The inference speed of large language models is constrained by the nature of autoregressive generation, which can only produce one token at a time and depends on all preceding tokens, making true parallelization impossible.

Models can still experience stitching-type hallucinations when integrating multiple retrieved results, so RAG is not the ultimate solution for eliminating hallucinations.

The transition problem in AI video stems from the autoregressive generation approach of models, where each video clip is independently sampled and generated, lacking cross-clip temporal consistency constraints.

Autoregressive generation refers to the model predicting the next token each time based on all previously generated tokens, progressively unfolding until a complete code block is generated

The generation phase is essentially a serial process that cannot be fully parallelized the way input encoding can, resulting in relatively low GPU utilization—this is the technical reason why output costs are inherently higher than input costs.

GPT-4 and Claude are large language models that can understand context, generate structured outputs, and execute multi-step reasoning

Output Tokens are typically 2-4x more expensive than input Tokens because generation requires autoregressive decoding Token by Token, with each generated Token requiring a complete forward pass computation

During the autoregressive decoding phase, GPU compute utilization is often less than 10%, making it a memory-intensive operation.

在自回归生成中，随着文本长度增加，早期设定的约束条件在概率分布中的权重逐渐降低，导致生成内容偏离原始意图

In language modeling, the Transformer is essentially learning the conditional probability distribution P(xt|x1,...,xt-1) of token sequences, and autoregressive generation is the process of sequentially sampling from this probability distribution.

Output tokens are typically more expensive than input tokens because outputs require autoregressive token-by-token generation, which has higher computational intensity and lower GPU utilization.

Goal Drift in AI Agents stems from the autoregressive generation mechanism of large language models, where small early deviations are progressively amplified during long sequence generation.

The topic drift phenomenon that occurs in long-text generation by large language models is academically referred to as Semantic Drift, and its root cause lies in the autoregressive generation mechanism.

The serial generation nature of autoregressive next-token prediction means each token requires a forward computation, resulting in significant latency when generating long texts.

In autoregressive generation, each token requires a full forward computation, and inference latency is proportional to generation length, making it the main throughput bottleneck.

The autoregressive generation phase is a memory-bandwidth-sensitive workload: generating each token requires reading the model weights in full once (or at least reading the weights involved in the activation path), so the actual speed is approximately equal to the VRAM bandwidth divided by the weight volume that needs to be read per token.

The autoregressive generation mechanism of language models is essentially predicting the next token based on local context, lacking a persistently effective explicit goal representation that spans multiple rounds of interaction.

Autoregressive generation is serial, so latency grows linearly when generating long texts.

NTP is the training objective and autoregressive generation is the inference method, both based on the same mechanism of predicting the next token from the preceding context.