This addresses a real UX problem that many people experience with LLMs - verbose, meandering responses that include unnecessary qualifications and caveats. The approach of using a constrained token budget or length penalty is straightforward, but I'm curious about the trade-offs.
One thing worth considering: sometimes LLMs are verbose because they're being uncertain about something. A model that's been constrained to be terse might come across as more confident than it actually is, which could be problematic in domains where calibrated uncertainty is important (like medical or legal advice).
The most interesting approach would be reward-model-based fine-tuning where you're directly optimizing for concise and accurate responses rather than just limiting token output. That way you're training the model to be efficient in its explanation, not just cutting off mid-thought.
Still, this is a nice practical demonstration that simple techniques can work. Token limits are an easy win for most chat applications.
alphadatavault•1d ago
One thing worth considering: sometimes LLMs are verbose because they're being uncertain about something. A model that's been constrained to be terse might come across as more confident than it actually is, which could be problematic in domains where calibrated uncertainty is important (like medical or legal advice).
The most interesting approach would be reward-model-based fine-tuning where you're directly optimizing for concise and accurate responses rather than just limiting token output. That way you're training the model to be efficient in its explanation, not just cutting off mid-thought.
Still, this is a nice practical demonstration that simple techniques can work. Token limits are an easy win for most chat applications.