Why Does Surprisal From Larger Transformer-Based Language Models Provide a Poorer Fit to Human Reading Times? (2023)

Abstract: This work presents a linguistic analysis into why larger Transformer-based pre-trained language models with more parameters and lower perplexity nonetheless yield surprisal estimates that are less predictive of human reading times. First, regression analyses show a strictly monotonic, positive log-linear relationship between perplexity and fit to reading times for the more recently released five GPT-Neo variants and eight OPT variants on two separate datasets, replicating earlier results limited to just GPT-2 (Oh et al., 2022). Subsequently, analysis of residual errors reveals a systematic deviation of the larger variants, such as underpredicting reading times of named entities and making compensatory overpredictions for reading times of function words such as modals and conjunctions. These results suggest that the propensity of larger Transformer-based models to ‘memorize’ sequences during training makes their surprisal estimates diverge from humanlike expectations, which warrants caution in using pre-trained language models to study human language processing.

## introduction expectation-based theories of sentence processing @xcite postulate that processing difficulty is largely driven by how predictable upcoming linguistic material is given its context. in cognitive modeling, predictability operationalized by information-theoretic surprisal @xcite has been shown to be a strong predictor of behavioral and neural measures of processing difficulty @xcite @xcite , providing empirical support for this position. as language models (lms) directly define a conditional probability distribution of a word given its context required for surprisal calculation, they have frequently been evaluated as surprisal-based cognitive models of sentence processing. recently, it was observed that surprisal from larger variants of the pre-trained gpt-2 lm @xcite that have more parameters and achieve lower perplexity is less predictive of self-paced reading times and eye-gaze durations collected during naturalistic reading @xcite . as the different variants of the pretrained gpt-2 model share the primary architecture and training data, this offers an especially strong counterexample to previous work that showed a negative relationship between lm perplexity and predictive power of surprisal estimates @xcite @xcite . more broadly, this observation also contradicts the recent 'larger is better' trend of the nlp community, leaving open the question of why larger lms perform worse. however, the @xcite results were part of a follow-up analysis in support of a separate claim about parser surprisal that only examined four model variants, so the results were not tested for statistical significance or extensively explored. the current work fills that gap by conducting a detailed linguistic analysis of the positive relationship between lm perplexity and predictive power of surprisal estimates. first, the robustness of the trend observed in @xcite is examined by reproducing their results and additionally evaluating surprisal estimates from different families of transformer-based lms (gpt-neo, opt; @xcite @xcite on their ability to predict human reading times. results from regression analyses show a strictly monotonic, positive log-linear relationship between lm perplexity and fit to reading times for the five gpt-neo variants and eight opt variants on two separate datasets, which provides firm empirical support for this trend. subsequently, to provide an explanation for this positive relationship, residual errors from the regression models are analyzed with a focus on identifying linguistic phenomena that surprisal from larger variants accounts for less accurately compared to surprisal from their smaller counterparts. the results show that regression models with surprisal predictors from gpt-2, gpt-neo, and opt models generally underpredict reading times at nouns and adjectives, and that the degree of underprediction increases along with model size. this indicates that the poorer fit to human reading times achieved by surprisal estimates from larger transformer-based lms is primarily driven by their characteristic of assigning lower surprisal values to open-class words, which may be accurately predicted by extensive domain knowledge gleaned from large sets of training examples that are not available to humans. this suggests that as transformer-based lms get larger, they may be problematic for cognitive modeling because they are trained with non-human learning objectives and different inductive biases on vast quantities of internet text. foot_0 ## related work in previous studies, surprisal estimates from several well-established types of lms, including n-gram models, simple recurrent networks @xcite , gated recurrent unit networks (gru; @xcite , and long short-term memory networks (lstm; @xcite , have been compared against behavioral measures of processing difficulty (e.g., @xcite @xcite . recently, as transformer-based @xcite models have dominated many nlp tasks, both large pre-trained and smaller 'trained-from-scratch' transformer-based lms have also been evaluated as models of processing difficulty @xcite @xcite . a consistent finding that emerged out of these studies is that better lms are also more predictive models of comprehension difficulty, or in other words, there is a negative correlation between lm perplexity and fit to human reading times. @xcite compared surprisal estimates from a set of n-gram and lstm lms and observed a negative linear relationship between perplexity and regression model fit. wilcox et al. (2020) evaluated n-gram, lstm, transformer, and rnng @xcite models and replicated the negative relationship, although they note a more exponential relationship at certain intervals. @xcite provided further support for this trend using gru and transformer models with different numbers of layers. 2 however, @xcite observed a directly contradictory relationship to this using surprisal estimates from pre-trained gpt-2 models @xcite . using self-paced reading times from the natural stories corpus @xcite and go-past durations from the dundee corpus @xcite , the authors calculated the increase in log-likelihood (δll) to a baseline linear-mixed effects (lme) model due to including a surprisal predictor. their results showed that surprisal from the largest xl variant made the smallest contribution to regression model fit, followed by the smaller large, medium, and small variants in that order, revealing a robust positive correlation between lm perplexity and predictive power of surprisal estimates. the same trend was replicated when unigram surprisal was included in the baseline, as well as when spillover effects were controlled for through the use of continuous-time deconvolutional regression (cdr; @xcite . moreover, recent work has shown that surprisal from neural lms generally tends to underpredict human reading times of both targeted constructions and naturalistic text. for instance, van schijndel and linzen (2021) and @xcite observed that surprisal from neural lms severely underpredicts the magnitude of garden-path effects demonstrated by human subjects. additionally, @xcite showed that surprisal from the pre-trained gpt-2 model fails to accurately predict the increase in reading times at the main verb of deeply embedded sentences. @xcite also demonstrated that neural lms yield surprisal estimates that underpredict naturalistic reading times of english and japanese text compared to those from neural lms that have a recency bias implemented as limited access to the previous context. ## main experiment: predictive power of language model surprisal estimates in order to examine whether the positive correlation observed by @xcite and others generalizes to larger transformer-based models, surprisal predictors from different variants of the gpt-2, gpt-neo, and opt lms were evaluated on self-paced reading times from the natural stories corpus @xcite and go-past eye-gaze durations from the dundee corpus @xcite . ## post-hoc analysis: linguistic phenomena underlying the trend in order to provide an explanation for the trend observed in section 3, the residual errors from the regression models were analyzed to identify data points that surprisal from larger lm variants accounted for less accurately compared to surprisal from their smaller counterparts. for this analysis, a special emphasis was placed on identifying subsets of data points where surprisal from larger lm variants deviated more drastically from humanlike processing difficulty. ## discussion and conclusion this work presents results using multiple large pre-trained lms showing that larger variants with more parameters and better next-word prediction performance (i.e., lower perplexity) nonetheless yield surprisal estimates that are less predictive of human reading times (i.e., smaller contribution to regression model fit), corroborating and expanding upon earlier results based on the gpt-2 lm @xcite . first, in order to examine the generalizability of this trend, surprisal estimates from five variants of the gpt-neo lm and eight variants of the opt lm were evaluated in terms of their ability to predict self-paced reading times and eye-gaze durations. the regression analysis revealed a strictly monotonic, positive log-linear relationship between perplexity and fit to reading times for five gpt-neo variants and eight opt variants, providing robust empirical support for this trend. additionally, the different data used to train each lm family seem to influence the quality of surprisal estimates, although more pre-training data did not necessarily result in surprisal estimates that are more predictive of reading times. subsequently, to identify the data points that are responsible for the positive relationship, a post-hoc analysis of the residual errors from each regression model was conducted. the results showed that the difference in mses between regression models containing surprisal predictors from different lm variants was especially large on nouns and adjectives, such as named entity terms and predicative adjectives. a further inspection of their predictions showed that the trend of mses on these words was driven mainly by underpredictions of reading time delays, which were exacerbated as the larger lm variants predicted the words more accurately and assigned lower surprisal values. this tendency also led to higher regression coefficients for surprisal estimates from larger lm variants, which resulted in a systematic overprediction at function words like conjunctions and modals that had similar surprisal estimates across lm variants. the 'more and more superhuman' predictions of larger lm variants observed in this work are consistent with findings from recent analyses of transformer-based lms. for example, a mathematical analysis of transformers @xcite showed that a layer of self-attention essentially functions as a lookup table that keeps track of bigram statistics of the input data. given this observation, it may be the case that the larger lm variants with more attention heads at their disposal have the capability to learn stronger local associations between tokens. this possibility was empirically supported from the perspective of memorization by @xcite , who found that larger variants of the gpt-neo model returned more sequences verbatim from the pre-training data during greedy decoding. this behavior may explain why nouns and adjectives showed the strongest effect in the post-hoc analysis; since adjectives and nouns typically have higher type-frequency than verbs or function words, it may be the case that nouns and adjectives that are rarely seen during training are predicted much more faithfully by the larger lm variants with higher model capacity. additionally, this also suggests that as these pre-trained lms continue to get bigger, they will continue to degrade as models of humanlike language comprehension. the 'trained-from-scratch' lms studied in earlier psycholinguistic modeling work (e.g., @xcite show a negative relationship between perplexity and fit to reading times. however, based on regression results following the same protocols as section 3, surprisal estimates from lms trained in @xcite generally seem to be less predictive of human reading times than those from pre-trained lms examined in this work. given the especially large discrepancy in model size between newly trained lms and pre-trained lms, it may be the case that they capture two distinct regimes in terms of the relationship between lm performance and predictive power of surprisal estimates. while the results of the current study clearly show that surprisal estimates from smaller pre-trained lm variants are more predictive of reading times, it remains to be seen how much smaller lms can become before the predictive power of surprisal estimates starts to decrease. with recently increasing effort in developing efficient nlp models, future work could explore the extent to which, for example, knowledge distillation techniques @xcite can result in lms that are more predictive of humanlike processing difficulty. additionally, the importance of being 'adequately surprised' at nouns like named entity terms that was identified in the current study may also explain similar recent counterexamples to the trend observed between model perplexity and fit to reading times @xcite . @xcite showed that incorporating a character model to estimate word generation probabilities within an incremental left-corner parser resulted in more predictive surprisal estimates compared to those from a baseline parser that treats words as symbols, although at a cost of higher test perplexity. the character model may be effectively assigning higher surprisal values to these rare words, thereby achieving better fit to human reading times. the reading times of japanese text studied in @xcite were measured in larger units (i.e., bunsetsu; roughly equivalent to phrases) than typical english words. therefore, the japanese lms analyzed in that study are likely to have been trained on and have made predictions on text that has been tokenized into 'sub-bunsetsu' tokens, which may have made a different picture emerge from results based on purely word-based lms of earlier work. in general, the tendency of pre-trained lm surprisal to underpredict reading times observed in this work is consistent with recent empirical shortcomings of neural lm surprisal. for example, van schijndel and linzen (2021) and @xcite found that surprisal from neural lms severely underpredicts the magnitude of garden-path effects demonstrated by human subjects. similarly, @xcite showed that surprisal from gpt-2 fails to accurately predict the increase in reading times at the main verb of deeply embedded sentences. @xcite also demonstrated that implementing a recency bias by deterministically truncating the context window of neural lms leads to surprisal estimates that alleviate the underpredictions of full neural lm surprisal on naturalistic reading times of english and japanese text. taken together, these results suggest that neural lms do not make abstract, linguistic generalizations like people do. moreover, there are also efforts to evaluate other memory-and attention-based predictors calculated from transformer-based lm representations on their ability to predict human behavior. for instance, ryu and @xcite drew connections between the self-attention mechanism of transformers and cue-based retrieval models of sentence comprehension (e.g., @xcite . their proposed attention entropy, which quantifies the diffuseness of attention weights over previous tokens, was found to show profiles that are consistent with similarity-based interference observed during the processing of subject-verb agreement. @xcite expanded upon this idea and showed that the entropy of attention weights at a given timestep as well as the shift in attention weights across consecutive timesteps are robust predictors of naturalistic reading times over gpt-2 surprisal. @xcite calculated the norm of the gradient of each input token on two eye-tracking corpora using bert @xcite as a metric of saliency, which showed higher correlations to fixation durations compared to raw attention weights. finally, it is becoming more common in psycholinguistic modeling to use surprisal from pre-trained lms as a baseline predictor to study various effects in naturalistic sentence processing (e.g., @xcite . the broader implication of the current study is that researchers should not select the largest pre-trained lm available based on the widely held 'larger is better' assumption of the nlp community. as a general practice, surprisal estimates from smaller pre-trained lm variants should be incorporated to form a more rigorous baseline, which will guard against drawing unwarranted scientific conclusions.