Human language use is robust to errors: comprehenders can and do mentally correct utterances that are implausible or anomalous. How are humans able to solve these problems in real time, picking out alternatives from an unbounded space of options using limited cognitive resources? And can language models trained on next-word prediction for typical language be augmented to handle language anomalies in a human-like way? Using a language model as a prior and an error model to encode likelihoods, we use Sequential Monte Carlo with optional rejuvenation to perform incremental and approximate probabilistic inference over intended sentences and production errors. We demonstrate that the model captures previously established patterns in human sentence processing, and that a trade-off between human-like noisy-channel inferences and computational resources falls out of this model. From a psycholinguistic perspective, our results offer a candidate algorithmic model of rational inference in language processing. From an NLP perspective, our results showcase how to elicit human-like noisy-channel inference behavior from a relatively small LLM while controlling the amount of computation available during inference. Our model is implemented in the Gen.jl probabilistic programming language, and our code is available at https://github.com/thomashikaru/noisy_channel_model.

Pragmatics and non-literal language understanding are essential to human communication, and present a long-standing challenge for artificial language models. We perform a fine-grained comparison of language models and humans on seven pragmatic phenomena, using zero-shot prompting on an expert-curated set of English materials. We ask whether models (1) select pragmatic interpretations of speaker utterances, (2) make similar error patterns as humans, and (3) use similar linguistic cues as humans to solve the tasks. We find that the largest models achieve high accuracy and match human error patterns: within incorrect responses, models favor literal interpretations over heuristic-based distractors. We also find preliminary evidence that models and humans are sensitive to similar linguistic cues. Our results suggest that pragmatic behaviors can emerge in models without explicitly constructed representations of mental states. However, models tend to struggle with phenomena relying on social expectation violations.

Language models (LMs) have been argued to overlap substantially with human beings in grammaticality judgment tasks. But when humans systematically make errors in language processing, should we expect LMs to behave like cognitive models of language and mimic human behavior? We answer this question by investigating LMs’ more subtle judgments associated with “language illusions” – sentences that are vague in meaning, implausible, or ungrammatical but receive unexpectedly high acceptability judgments by humans. We looked at three illusions: the comparative illusion (e.g. “More people have been to Russia than I have”), the depth-charge illusion (e.g. “No head injury is too trivial to be ignored”), and the negative polarity item (NPI) illusion (e.g. “The hunter who no villager believed to be trustworthy will ever shoot a bear”). We found that probabilities represented by LMs were more likely to align with human judgments of being “tricked” by the NPI illusion which examines a structural dependency, compared to the comparative and the depth-charge illusions which require sophisticated semantic understanding. No single LM or metric yielded results that are entirely consistent with human behavior. Ultimately, we show that LMs are limited both in their construal as cognitive models of human language processing and in their capacity to recognize nuanced but critical information in complicated language materials.

Studies on the role of memory as a predictor of reading time latencies (1) differ in their predictions about when memory effects should occur in processing and (2) have had mixed results, with strong positive effects emerging from isolated constructed stimuli and weak or even negative effects emerging from naturally-occurring stimuli. Our study addresses these concerns by comparing several implementations of prominent sentence processing theories on an exploratory corpus and evaluating the most successful of these on a confirmatory corpus, using a new self-paced reading corpus of seemingly natural narratives constructed to contain an unusually high proportion of memory-intensive constructions. We show highly significant and complementary broad-coverage latency effects both for predictors based on the Dependency Locality Theory and for predictors based on a left-corner parsing model of sentence processing. Our results indicate that memory access during sentence processing does take time, but suggest that stimuli requiring many memory access events may be necessary in order to observe the effect.