# When a sentence does not introduce a discourse entity, Transformer-based models still sometimes refer to it

arXiv: https://arxiv.org/abs/2205.03472 · HTML: https://mlsanity.com/paper/2205.03472/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 42 references that are in the map.
- First posted on arXiv 6 May 2022.

## Builds on (most cited first, 26 of 26)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://mlsanity.com/paper/1901.02860/) (2019)
- [BERT Rediscovers the Classical NLP Pipeline](https://mlsanity.com/paper/1905.05950/) (2019)
- [What Does BERT Look At? An Analysis of BERT's Attention](https://mlsanity.com/paper/1906.04341/) (2019)
- [The LAMBADA dataset: Word prediction requiring a broad discourse context](https://mlsanity.com/paper/1606.06031/) (2016)
- [BLiMP: The Benchmark of Linguistic Minimal Pairs for English](https://mlsanity.com/paper/1912.00582/) (2019)
- [Memory Networks](https://mlsanity.com/paper/1410.3916/) (2014)
- [Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies](https://mlsanity.com/paper/1611.01368/) (2016)
- [What BERT is not: Lessons from a new suite of psycholinguistic diagnostics for language models](https://mlsanity.com/paper/1907.13528/) (2019)
- [All That's 'Human' Is Not Gold: Evaluating Human Evaluation of Generated Text](https://mlsanity.com/paper/2107.00061/) (2021)
- [Targeted Syntactic Evaluation of Language Models](https://mlsanity.com/paper/1808.09031/) (2018)
- [The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics](https://mlsanity.com/paper/2102.01672/) (2021)
- [Colorless green recurrent networks dream hierarchically](https://mlsanity.com/paper/1803.11138/) (2018)
- [Implicit Representations of Meaning in Neural Language Models](https://mlsanity.com/paper/2106.00737/) (2021)
- [A Systematic Assessment of Syntactic Generalization in Neural Language Models](https://mlsanity.com/paper/2005.03692/) (2020)
- [Neural Language Models as Psycholinguistic Subjects: Representations of Syntactic State](https://mlsanity.com/paper/1903.03260/) (2019)
- [PlotMachines: Outline-Conditioned Generation with Dynamic Plot State Tracking](https://mlsanity.com/paper/2004.14967/) (2020)
- [What do RNN Language Models Learn about Filler-Gap Dependencies?](https://mlsanity.com/paper/1809.00042/) (2018)
- [Provable Limitations of Acquiring Meaning from Ungrounded Form: What Will Future Language Models Understand?](https://mlsanity.com/paper/2104.10809/) (2021)
- [Probing What Different NLP Tasks Teach Machines about Function Word Comprehension](https://mlsanity.com/paper/1904.11544/) (2019)
- [Refining Targeted Syntactic Evaluation of Language Models](https://mlsanity.com/paper/2104.09635/) (2021)
- [Sorting through the noise: Testing robustness of information processing in pre-trained language models](https://mlsanity.com/paper/2109.12393/) (2021)
- [Pragmatic competence of pre-trained language models through the lens of discourse connectives](https://mlsanity.com/paper/2109.12951/) (2021)
- [The Language Model Understood the Prompt was Ambiguous: Probing Syntactic Uncertainty Through Generation](https://mlsanity.com/paper/2109.07848/) (2021)
- [Attending to Entities for Better Text Understanding](https://mlsanity.com/paper/1911.04361/) (2019)

## Built on by (most cited first, 23 of 23)

- [Neural Theory-of-Mind? On the Limits of Social Intelligence in Large LMs](https://mlsanity.com/paper/2210.13312/) (2022)
- [Language Models as Agent Models](https://mlsanity.com/paper/2212.01681/) (2022)
- [Language Model Behavior: A Comprehensive Survey](https://mlsanity.com/paper/2303.11504/) (2023)
- [Eight Things to Know about Large Language Models](https://mlsanity.com/paper/2304.00612/) (2023)
- [Entity Tracking in Language Models](https://mlsanity.com/paper/2305.02363/) (2023)
- [Can language models handle recursively nested grammatical structures? A case study on comparing models and humans](https://mlsanity.com/paper/2210.15303/) (2022)
- [Dissociating language and thought in large language models](https://mlsanity.com/paper/2301.06627/) (2023)
- [Model Criticism for Long-Form Text Generation](https://mlsanity.com/paper/2210.08444/) (2022)
- [Scope Ambiguities in Large Language Models](https://mlsanity.com/paper/2404.04332/) (2024)
- [RST-LoRA: A Discourse-Aware Low-Rank Adaptation for Long Document Abstractive Summarization](https://mlsanity.com/paper/2405.00657/) (2024)
- [Do Large Language Models Mirror Cognitive Language Processing?](https://mlsanity.com/paper/2402.18023/) (2024)
- [Understanding Natural Language Understanding Systems. A Critical Analysis](https://mlsanity.com/paper/2303.04229/) (2023)
- [Experimental Contexts Can Facilitate Robust Semantic Property Inference in Language Models, but Inconsistently](https://mlsanity.com/paper/2401.06640/) (2024)
- [Hey, wait a minute: on at-issue sensitivity in Language Models](https://mlsanity.com/paper/2510.12740/) (2025)
- [Mary, the Cheeseburger-Eating Vegetarian: Do LLMs Recognize Incoherence in Narratives?](https://mlsanity.com/paper/2512.07777/) (2025)
- [Language Models Largely Exhibit Human-like Constituent Ordering Preferences](https://mlsanity.com/paper/2502.05670/) (2025)
- [Meaning Beyond Truth Conditions: Evaluating Discourse Level Understanding via Anaphora Accessibility](https://mlsanity.com/paper/2502.14119/) (2025)
- [On General Language Understanding](https://mlsanity.com/paper/2310.18038/) (2023)
- [LIEDER: Linguistically-Informed Evaluation for Discourse Entity Recognition](https://mlsanity.com/paper/2403.06301/) (2024)
- [To Drop or Not to Drop? Predicting Argument Ellipsis Judgments: A Case Study in Japanese](https://mlsanity.com/paper/2404.11315/) (2024)
- [Do Large Language Models know who did what to whom?](https://mlsanity.com/paper/2504.16884/) (2025)
- [A Circuit for Plural Reference: How LLMs Represent and Retrieve Singular and Plural Entities](https://mlsanity.com/paper/2609.03687/) (2026)
- [Not Just an Evaluation: A Case Study of Discourse Particle Sensitivity in Language Models](https://mlsanity.com/paper/2610.13794/) (2026)

## 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.