Incremental Sentence Processing Mechanisms in Autoregressive Transformer Language Models (2025)

Abstract: Autoregressive transformer language models (LMs) possess strong syntactic abilities, often successfully handling phenomena from agreement to NPI licensing. However, the features they use to incrementally process their linguistic input are not well understood. In this paper, we fill this gap by studying the mechanisms underlying garden path sentence processing in LMs. Specifically, we ask: (1) Do LMs use syntactic features or shallow heuristics to perform incremental sentence processing? (2) Do LMs represent only one potential interpretation, or multiple? and (3) Do LMs reanalyze or repair their initial incorrect representations? To address these questions, we use sparse autoencoders to identify interpretable features that determine which continuation—and thus which reading—of a garden path sentence the LM prefers. We find that while many important features relate to syntactic structure, some reflect syntactically irrelevant heuristics. Moreover, though most active features correspond to one reading of the sentence, some features correspond to the other, suggesting that LMs assign weight to both possibilities. Finally, LMs fail to re-use features to answer follow-up questions.

## introduction syntactic ambiguities abound in natural language. for example, given the fragment "after the woman drank the water. . . ", the water could be either the object of drank (in which case one could end the sentence here), or the subject of the main clause (in which case "was all gone" would be a valid continuation). despite lms' impressive performance on syntactic tasks @xcite , the mechanisms that underlie their processing of syntactic structure-and temporary ambiguities therein-are not well understood. past work has found lm attention heads dedicated to processing certain syntactic relations @xcite and used lms' representational structure to predict dependency relations (hewitt and manning, 2019); nonetheless, these results only show that structural information can be extracted from lm representations-and not that these representations are causally implicated in lm processing. it thus remains unclear whether lms rely on structure-related features, represent the multiple possible completions to an incomplete ambiguous utterance, or revise representations in light of new disambiguating evidence. in the psycholinguistics literature, similar questions have been studied in humans using garden path sentences, which initially appear to have one structure, but which are later revealed to have another. when humans encounter the unexpected resolution of these sentences, their reading is delayed. different theories of human sentence processing predict different delays; by recording reading times on carefully designed test materials, one can thus empirically test such theories @xcite . while prior work on lms has used garden path sentences as a testbed for the psychometric fit of lm surprisals to predict human reading times @xcite @xcite , we propose to instead use them to understand how lms incrementally process sentences. in this study, we present a mechanistic investigation of how lms incrementally process sentences and how they handle temporary ambiguities using garden path (gp) sentences as a case study. using sparse autoencoders and causal interpretability methods, we uncover the causally relevant features (and mechanisms composed thereof) that explain why lms assign higher probabilities to particular completions. with these methods, we investigate 3 research questions (rqs), and find the following: rq1: do lms use syntactic features or spuriwe use sparse autoencoders to decompose model activations into a discrete set of humaninterpretable components (features). we score each feature by its causal contribution to continuations associated with each reading of a garden path sentence. we manually interpret the top-scoring features and causally verify their functional role in the network by targetedly up-or downweighting them to change the model's preferred reading. rq2: do lms hold on to multiple interpretations of the sentence simultaneously, or commit to the most likely one? lms' representations encode multiple interpretations simultaneously. rq3: given disambiguating evidence, do lms repair or reanalyze their initial structural predictions? lms do not repair or rely on their prior structural predictions; however, they also do not generate new structural features via reanalysis. ## models to analyze incremental parsing in lms, we must study autoregressive lms. 3 we analyze pythia-70m-deduped @xcite , and gemma-2-2b (gemma @xcite , as these have publicly available saes. we focus primarily on pythia-70m in the main text due to its smaller size; results for gemma-2-2b are in app. e. we use garden path sentences to investigate incremental processing in lms because they contain temporary structural ambiguities that are eventually resolved. the sentences we study must be ambiguous such that we may determine if one or many possible readings are represented (rq2). moreover, this ambiguity must eventually be resolved such that we may study how incorrect representations might be handled (rq3). thus, garden path sentences are ideal stimuli with which to answer our rqs; indeed, they are often used for these purposes in psycholinguistics ( §2.1). ## general discussion and conclusions when conducting behavioral analyses, one must be cautious in (but not entirely averse to) imposing human-like cognitive abstractions onto lms. we observed that, despite high performance on syntactic evaluations, lms rely on both human-like syntactic abstractions and spurious features. indeed, many influential features activated on tokens before relevant syntactic information for either reading of the sentence had appeared. this underscores the importance of mechanistic study of lm behaviors: even when models perform well, it may not always be for the reasons that one might anticipate. we have seen that lms represent multiple interpretations of partial sentences. however, it remains unclear if lms deploy mechanisms that recognize or adjudicate between mutually exclusive possibilities. the representation-recognition distinction is crucial: ambiguity has many functions (e.g., humor and politeness), but to detect these, one must recognize ambiguity as a meaningful signal. we leave the question of recognition to future work. lms did not rely on prior features when answering garden path follow-up questions, indicating a lack of repair, but also did not generate any new syntactic features via reanalysis. while we cannot definitively rule out the existence of syntactic reanalysis circuits, such features appear uninfluential in the gprc circuits. we hope future advances in saes and automated interpretability will enable us to better understand sparse feature circuits at scale.