Neologism Learning as a Parameter-Efficient Alternative to Fine-Tuning for Model Steering
This addresses the need for flexible and compute-efficient model steering in AI, though it appears incremental as it builds on existing parameter-efficient fine-tuning methods.
The paper tackles the problem of steering language model behavior by introducing neologism learning as a parameter-efficient alternative to fine-tuning, finding that it outperforms low-rank adaptation (LoRA) under matched training conditions.
In language modeling, neologisms are new tokens trained to represent a concept not already included in a given model's vocabulary. Neologisms can be used to encourage specific behavior in models, for example by appending prompts with "Give me a neologism answer." Behavioral steering can also be achieved through fine-tuning, albeit with more compute and less flexibility: learning a neologism only trains d parameters and allows the user to still access the model's default behavior. We compare the performance of neologism learning against low-rank adaptation (LoRA) fine-tuning, finding that neologisms outperform fine-tuned models under a matched training setup (same data and hyperparameters). We also investigate self-verbalizations of neologisms, and observe that the model will occasionally make up its own new words when asked about a neologism.