Humans can quickly learn a new word from a few illustrative examples, and then systematically and flexibly use it in novel contexts. Yet the abilities of current language models for few-shot word learning, and methods for improving these abilities, are underexplored. In this study, we introduce a novel method, Meta-training for IN-context learNing Of Words (Minnow). This method trains language models to generate new examples of a word’s usage given a few in-context examples, using a special placeholder token to represent the new word. This training is repeated on many new words to develop a general word-learning ability. We find that training models from scratch with Minnow on human-scale child-directed language enables strong few-shot word learning, comparable to a large language model (LLM) pre-trained on orders of magnitude more data. Furthermore, through discriminative and generative evaluations, we demonstrate that finetuning pre-trained LLMs with Minnow improves their ability to discriminate between new words, identify syntactic categories of new words, and generate reasonable new usages and definitions for new words, based on one or a few in-context examples. These findings highlight the data efficiency of Minnow and its potential to improve language model performance in word learning tasks.

“Deploying tuning-free prompting is challenging in engineering practice: it not only requiresusers to engage in cumbersome trials and errors but is also extremely time-consuming,as even a slight change in wording and phrasing could have a huge impact on the finalperformance. To further investigate the impact of different prompts, in this work, weperform a systematic inspection of four factors in linguistics involved in prompt engineering:syntax, semantics, lexicon, and pragmatics. The empirical results quantify the sensitivityof the output to small textual perturbations in four linguistic factors of prompts. Basedon the analysis of these four factors, we present a series of design guidelines to helphuman users write effective prompts. Human evaluation on amateurs shows that usingthe proposed guidelines helps humans produce prompts with significant gains in zero-shotperformance in Pre-trained Language Models (PLMs) and hence validates the utility ofthe guidelines.”