When a sentence does not introduce a discourse entity, Transformer-based models still sometimes refer to it
Not on the map. Why?
Abstract (arXiv)
Understanding longer narratives or participating in conversations requires tracking of discourse entities that have been mentioned. Indefinite noun phrases (NPs), such as 'a dog', frequently introduce discourse entities but this behavior is modulated by sentential operators such as negation. For example, 'a dog' in 'Arthur doesn't own a dog' does not introduce a discourse entity due to the presence of negation. In this work, we adapt the psycholinguistic assessment of language models paradigm to higher-level linguistic phenomena and introduce an English evaluation suite that targets the knowledge of the interactions between sentential operators and indefinite NPs. We use this evaluation suite for a fine-grained investigation of the entity tracking abilities of the Transformer-based models GPT-2 and GPT-3. We find that while the models are to a certain extent sensitive to the interactions we investigate, they are all challenged by the presence of multiple NPs and their behavior is not systematic, which suggests that even models at the scale of GPT-3 do not fully acquire basic entity tracking abilities.
Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). Builds on 26 of its 42 references that are in the map. First posted on arXiv 6 May 2022.
Builds on
26 of 42 references.
References · 26
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Citing papers per year: 2022: 4; 2023: 6; 2024: 6; 2025: 5; 2026: 1.
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12 most cited of 18 All 18 (JSON)
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- 2024RST-LoRA: A Discourse-Aware Low-Rank Adaptation for Long Document Abstractive Summarization1 May 2024
- 2024To Drop or Not to Drop? Predicting Argument Ellipsis Judgments: A Case Study in Japanese17 Apr 2024
- 2024Scope Ambiguities in Large Language Models5 Apr 2024
- 2024LIEDER: Linguistically-Informed Evaluation for Discourse Entity Recognition10 Mar 2024
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12 newest of 18 All 18 (JSON)