Unverified50% confidenceFactExact time
In actual development, "tuning model parameters" mostly does not mean retraining the model, but rather adjusting call-side hyperparameters and prompts.
1
Sources
50%
Confidence
Long-term
Relevance
10/18/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedCertain specific phrasings in the original prompt activate shortcuts entrenched during model training, causing the model to apply fixed patterns in its responses; after paraphrasing, the model is forced to perform more general reasoning, thereby improving output quality.75% similarUnverifiedCross-prompt inference reduces dependence on the computation introduced by fine-tuning, pushing the model's computational emphasis back onto the pretrained components.75% similarVerifiedModel steering influences model output behavior during inference by directly modifying the activation vectors of intermediate layers, without modifying weights.74% similarUnverifiedA model's actual behavior is jointly determined by training weights, system prompts, and runtime context; looking at the prompt alone cannot fully reproduce the complete picture of the product.74% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1064076API
curl https://kongchang.com/api/v1/knowledge/claims/1064076MCP
get_claim(id=1064076)