Resolving anaphora requires integrating syntactic, semantic, and discourse information. Mandarin Chinese offers a particularly revealing case through the reflexive ziji, whose interpretation permits long-distance binding licensed by logophoric cues (i.e., cues relevant to discourse perspective). While these cues have been extensively studied in linguistic theory and psycholinguistic experiments, it remains an open question to what extent such cues are captured by computational models.We investigate this question by probing large language models’ sensitivity to four logophoric cues known to license long-distance binding of ziji: predicate type, perspective marking, discourse topicality, and discourse relation. Using minimal pairs and surprisal-based measures, we assess whether models exhibit systematic biases toward non-local antecedents in logophoric contexts.Across two model families, we find that (i) models exhibit above-chance sensitivity to all four cues; (ii) lexically anchored cues are more robustly captured than discourse-level cues; and (iii) some cues generalize cross-lingually, whereas others appear to depend on language-specific training data. Taken together, these findings provide non-English evidence that large language models capture certain aspects of logophoricity, yet continue to struggle with discourse-level representations that are central to human anaphora resolution. Code and data are available at: https://github.com/yunfang-dong/mandarin-logophoricity-llm

Previous work has shown that simple mask-filling can provide useful information about the discourse informativeness of syntactic structures. Dong et al. (2024) first adopted this approach to investigating preposing constructions. The problem with single token mask fillers was that they were, by and large, ambiguous. We address the issue by adapting the approach of Kalinsky et al. (2023) to support the prediction of multi-token connectives in masked positions. Our first experiment demonstrates that this multi-token mask-filling approach substantially outperforms the previously considered single-token approach in recognizing implicit discourse relations. Our second experiment corroborates previous findings, providing additional empirical support for the role of preposed syntactic constituents in signaling discourse coherence. Overall, our study extends existing mask-filling methods to a new discourse-level task and reinforces the linguistic hypothesis concerning the discourse informativeness of preposed structures.

Over 15 years ago, Ward & Birner (2006) suggested that non-canonical constructions in English can serve both to mark information status and to structure the information flow of discourse. One such construction is preposing, where a phrasal constituent appears to the left of its canonical position, typically sentence-initially. But computational work on discourse has, to date, ignored non-canonical syntax. We take account of non-canonical syntax by providing quantitative evidence relating NP/PP preposing to discourse relations. The evidence comes from an LLM mask-filling task that compares the predictions when a mask is inserted between the arguments of an implicit inter-sentential discourse relation — first, when the right-hand argument (Arg2) starts with a preposed constituent, and again, when that constituent is in canonical (post-verbal) position. Results show that (1) the top-ranked mask-fillers in the preposed case agree more often with “gold” annotations in the Penn Discourse TreeBank than they do in the latter case, and (2) preposing in Arg2 can affect the distribution of discourse-relational senses.