W ino D ict: Probing language models for in-context word acquisition (2023)

Abstract: We introduce a new in-context learning paradigm to measure Large Language Models’ (LLMs) ability to learn novel words during inference. In particular, we rewrite Winograd-style co-reference resolution problems by replacing the key concept word with a synthetic but plausible word that the model must understand to complete the task. Solving this task requires the model to make use of the dictionary definition of the new word given in the prompt. This benchmark addresses word acquisition, one important aspect of the diachronic degradation known to afflict LLMs. As LLMs are frozen in time at the moment they are trained, they are normally unable to reflect the way language changes over time. We show that the accuracy of LLMs compared to the original Winograd tasks decreases radically in our benchmark, thus identifying a limitation of current models and providing a benchmark to measure future improvements in LLMs ability to do in-context learning.

## introduction large language models (llms) such as gpt-3 @xcite and palm @xcite can only learn from information that is in their training corpus. however, this is naturally limiting because the training corpus itself is bounded in time to the point of its collection. as a result, recent work has studied how to adapt such models to new data without an expensive retraining phase. methods range from using semiparametric methods with access to external memory (e.g., @xcite , to continual learning (e.g., @xcite , to parameter efficient fine-tuning (e.g., ben @xcite . much of this work concerns factual knowledge or task distribution shifts. however, language also changes subtly: for instance, the popularity or meaning of individual words can change over time. in fact, such shifts also cause a consistent decrease in model performance for downstream tasks @xcite @xcite . acquiring new words through either examples or definitions is therefore an important test of llms' ability to overcome diachronic degradation. with in-context learning having emerged as the primary way to interact with llms @xcite , we propose to study llms capability of acquiring new vocabulary via prompting. we propose winodict, a novel benchmark for word acquisition for llms. word acquisition is challenging to study in a realistic setting as it is hard to know which terms a model has already been exposed to. to overcome this, we rely on a heuristic method to introduce newly invented words and define them in terms of existing concepts. following previous work @xcite , we incorporate the required knowledge into the prompt. we then ask models to perform tasks that require successfully interpreting the invented words. we consider the english co-reference resolution datasets winograd schema challenge @xcite and winogrande @xcite . the examples are built in pairs with minimal changes, which allow the identification of the key concept that must be understood to solve the example. an example of winodict can be seen in figure 1 . our contributions are the following: (a) we propose winodict, a method and dataset to test models for word acquisition skills. (b) we benchmark the performance of several stateof-the-art models across scale and number of shots. (c) we analyze the effect of prompt, pos tags, word likelihood and similarity for ease of acquisition. these results help us understand the challenges for incorporating new concepts into llms. the code to build the dataset has been open-sourced. 1 winograd the city councilmen refused the demonstrators a permit because they feared violence. the city councilmen refused the demonstrators a permit because they advocated violence. ## methods winodict, like winograd and winogrande, is a co-reference resolution task in a binary choice setup. a model is given two alternative noun phrases, and has to decide which one is more likely to correspond to a highlighted pronoun or blank. ## experiments in this work we test gpt-3 @xcite , and palm @xcite ) models of various sizes, ranging from 3b to 540b parameters. appendix a has more details on the model sizes. as in the original in-context learning evaluations, we try 0, 1, and 5-shot experiments, using random examples to build the prompt. we compare to both a zero-shot human evaluation as well as the original source datasets with only our filtered examples. the main experimental results are shown in we verify that omitting any information of the new word yields random results for even the best palm-540b model. we discuss this and other prompting strategies in more detail in appendix b. ## new word analysis several factors can affect the capabilities for word acquisition of llms. we investigate several attributes, split into quartiles, using palm-540b with 5-shots, which is the best model from we consider the following attributes: (1) the part-of-speech of the synthetic word; (2) the average model negative log likelihood (nll) of the two model predictions, which measures the likelihood of the suffix for both prefixes; (3) the number of sentencepiece @xcite tokens in the synthetic word, to investigate the effect of model tokenization; (4) the number of sentence-piece tokens in the definition of the synthetic word, to investigate if longer definitions are more challenging; (5) the levenshtein edit distance between the synthetic and original word, to investigate if similar words are easier; and (6) the likelihood of the new word as computed by our probabilistic model of three-letter sequences, to see if less probable words are more difficult to acquire. of the six attributes, the two most correlated with accuracy are (4) the definition length and (2) the average nll. we observe no clear pattern in the other four attributes. in figure 2 we show their effect in each quartile. the effect of definition length indicated that the 25% longest definitions are the hardest to acquire by a significant margin (12% relative drop for winograd, 5% for winogrande). the relative accuracy drop for the largest quartile of the nll average is 13% for winograd and 4% for winogrande. the drop in nll suggests that when models assigns low probabilities to answers, they make more mistakes: the low probability may indicate the model has a poor understanding of the prefix so scores the suffix randomly. ## related work word acquisition for llms. inspired by developmental linguistics @xcite , @xcite succeeded to prompt gpt-3 to generate plausible example sentences based on definitions of synthetic words. unlike winodict, the evaluation was purely qualitative. common sense. li et al. ( 2021 ) study how prompt structures and scoring methods affect the performance of llms on common sense tasks including winogrande, where they observe the least variation. the format from winograd has been subsequently used to probe models for other phenomena such as explanations @xcite and gender bias @xcite . benchmarks for lexical knowledge. schick and schütze (2020) introduce a benchmark for probing a model's knowledge of the properties of rare words. @xcite train models to match word and definition representations, which they apply to a reverse dictionary task. ## conclusion in this work, we study the question of in-context word acquisition by large language models. while non-trivial to measure, the ability to incorporate knowledge about new words in-context may be useful to decrease the effect of diachronic degradation. we design a mechanism to transform winogradstyle tasks into challenging probes for reasoning on the meaning assigned to synthetic words, allowing for a more objective measurement of word acquisition. we study the results of models of multiple sizes and families and conclude that while the problem becomes easier with scale, there is still a substantial gap with human performance and the original winograd and winogrande tasks, demonstrating the difficulty of the proposed task. finally, we show that acquiring novel definitions is of similar difficulty, indicating the task is realistic.