Lexical Generalization Improves with Larger Models and Longer Training (2022)

Abstract: While fine-tuned language models perform well on many language tasks, they were also shown to rely on superficial surface features such as lexical overlap. Excessive utilization of such heuristics can lead to failure on challenging inputs. We analyze the use of lexical overlap heuristics in natural language inference, paraphrase detection, and reading comprehension (using a novel contrastive dataset),and find that larger models are much less susceptible to adopting lexical overlap heuristics. We also find that longer training leads models to abandon lexical overlap heuristics. Finally, We provide evidence that the disparity between models size has its source in the pre-trained model.

## introduction pretrained language models (plms) dramatically improved the performances on a wide range of nlp tasks, resulting in benchmarks claiming to track progress of language understanding, like glue @xcite and squad @xcite to be "solved". however, many works show these models to be brittle and generalize poorly to "out-of-distribution examples" @xcite @xcite . one of the reasons for the poor generalization is models adopting superficial heuristics from the training data, such as, lexical overlap: simple match of words between two textual instances. for instance, a paraphrase-detection model that makes use of these heuristics may determine that two sentences are paraphrases of each other by simply comparing the bags-of-words of these sentences. while this heuristic sometimes works, it is also often wrong, as demonstrated in figure 1 . indeed, while such heuristics are effective for solving indomain large datasets, different tests expose that models often rely on these heuristics, and thus they 1 code and data are available at: https://github.com/ elronbandel/lexical-generalization . model, that relies on lexical overlap heuristic to make a (wrong) prediction. in contrast, model is larger or is trained for longer, and does not rely on lexical overlap and predicts correctly. in this work, we link the adoption of lexical overlap heuristic to size of plms and to the number of iterations in the fine-tuning process. we show that a lot of the benefit from the above methods can be achieved simply by using larger plms and finetuning them for longer. we show that larger plms, and longer trained models are much less prone to rely on lexical overlap, despite not being manifested on standard validation sets. we validate these findings on three widely used plms: bert @xcite , roberta @xcite , and albert @xcite and three tasks: natural language inference (nli), paraphrase detection (pd), and reading comprehension (rc). for rc, we collect a new contrastive test-set -alsqa, based on squad2.0 @xcite , while controlling on the lexical overlap between the texts and the questions, containing 365 examples. ## related work this work relates to a line of research using behavioral methods for understanding model behavior @xcite @xcite , and more specifically regarding the extent in which specific heuristics being used by models for prediction @xcite @xcite @xcite . the use of heuristics such as lexical overlap also typically point on the lexical and under sensitivity of models @xcite , studied in different setups @xcite @xcite @xcite @xcite . our work suggests that the size of plms affects their inductive bias @xcite towards the preferred strategy. studies of inductive biases in nlp has gained attention recently @xcite @xcite @xcite . while @xcite studied the effect of the amount of pre-training data on linguistic generalization, we show that additional training iterations and larger models affects their inductive biases with respect to the use of lexical overlap heuristics. finally, @xcite show that the adoption of heuristics can be explained by the ability to generalize from a few examples that aren't aligned with the heuristics. they show that larger model size and longer fine-tuning can marginally increase the ability of the model to generalize from minority groups, an insight also discussed also by @xcite . our focus of lexical heuristics exclusively, together with robust fine-grained experimental setup concludes, unlike @xcite , that the change in lexical heuristic adoption behavior is consistent and non marginal. ## measuring reliance on lexical overlap definition: reliance on lexical overlap we say that a model makes use of lexical heuristics if it relies on superficial lexical cues in the text by making use of their identity rather than the semantics of the text. ## experiments and results setup we experiment with 3 strong plms: bert @xcite , roberta @xcite and electra @xcite , each with a few size variants. since performance after finetuning can vary, both for in-domain @xcite , and out-of-domain the nli and pd are text-pair classification tasks. however, rc is a span prediction task, as such, we explore two versions of this task: (1) the regular span prediction task, and (2) a text-pair classification task, where the goal is to predict if the questions is answerable or not from the text, which we call answerability. finding i: larger is better (but it is not reflected on the dev set) we report the results in finding ii: longer is better (but it is not reflected on the dev set) next, we compare the early (after one iterations over the training data) and late (after six iterations) versions of models. to ease comparison, the ∆ column indicates the difference between the heur score on the same model size, at the early and late stages of training. the differences show a constant improvement on this score from early to later versions of the trained models. accordingly, the improvements on the standard development sets are again much smaller: for instance, on nli using bert-large the dev-score increases by 1% while the heur score improves relatively by 77% (see figure 4 ) 4 this indicates that models tend to adopt 4 the trend seems to not hold in electra-large in ## conclusions and discussion we show that (1) larger pre-trained models are less prone to make use of lexical overlap heuristics in fine-tuning; and (2) longer fine-tuning may reduce the use of such heuristics. these findings raise questions on the training dynamics of fine-tuned lms @xcite , which should be explored in future work: what kind of inductive biases does large models posses towards better generalization abilities? what makes them adopt and abandon overlap heuristics with minimal signal from the training data? we suggest that larger models are better at learning from improbable groups in the data due to their larger capacity. to support this claim we show that larger plms achieve better perplexity mainly in less probable sentences. our work is the first to suggest that sufficiently-trained large plms are capable at arriving at solutions that are not wrongly reliant on lexical overlap. such models should be used as baselines when developing techniques to alleviate the reliance of lexical heuristics @xcite @xcite and assessing progress @xcite .