Output Length Control: Stop LLMs Over-Generating & Wasting Tokens

Output length control for large language models (LLMs) is a strategy that involves limiting the number of tokens generated per response to reduce costs without sacrificing quality, achieved through techniques like system prompt instructions, structured formats, stop sequences, few-shot examples, and setting the max_tokens parameter. The necessity for this control arises from the tendency of models, particularly those trained with reinforcement learning from human feedback (RLHF), to produce excessively verbose outputs due to a length bias where human annotators prefer longer responses. This verbosity incurs significant costs, as output tokens are substantially more expensive than input tokens, with costs being 2-6 times higher. Various methods are employed to manage output length, such as explicit length constraints, structured output formats like JSON, few-shot length calibration, employing stop sequences, and disciplined use of the max_tokens parameter. Implementing these controls can cut output length by 40-74% while maintaining response quality, especially when enforced at the gateway layer across all models and teams, which prevents wasteful token generation and ensures consistency in application.

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