mlsanityData to 7 Oct 2026

Natural Language Decompositions of Implicit Content Enable Better Text Representations

arXiv 2305.14583 ↗First posted 23 May 2023cs.CLcited by 18

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Abstract (arXiv)

When people interpret text, they rely on inferences that go beyond the observed language itself. Inspired by this observation, we introduce a method for the analysis of text that takes implicitly communicated content explicitly into account. We use a large language model to produce sets of propositions that are inferentially related to the text that has been observed, then validate the plausibility of the generated content via human judgments. Incorporating these explicit representations of implicit content proves useful in multiple problem settings that involve the human interpretation of utterances: assessing the similarity of arguments, making sense of a body of opinion data, and modeling legislative behavior. Our results suggest that modeling the meanings behind observed language, rather than the literal text alone, is a valuable direction for NLP and particularly its applications to social science.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 26 of its 82 references that are in the map. First posted on arXiv 23 May 2023.

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26 of 82 references.

References · 26

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
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  3. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
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  8. 2019How Can We Know What Language Models Know?cited by 953
  9. 2023Whose Opinions Do Language Models Reflect?cited by 759
  10. 2021Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Modelscited by 462
  11. 2019COMET: Commonsense Transformers for Automatic Knowledge Graph Constructioncited by 450
  12. 2019PAWS: Paraphrase Adversaries from Word Scramblingcited by 395
14 more
  1. 2022WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creationcited by 200
  2. 2016Enhanced LSTM for Natural Language Inferencecited by 199
  3. 2020Generation-Augmented Retrieval for Open-domain Question Answeringcited by 186
  4. 2021Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive Summarizationcited by 128
  5. 2019Analyzing Polarization in Social Media: Method and Application to Tweets on 21 Mass Shootingscited by 67
  6. 2022Generating Literal and Implied Subquestions to Fact-check Complex Claimscited by 66
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  8. 2022SBERT studies Meaning Representations: Decomposing Sentence Embeddings into Explainable Semantic Featurescited by 33
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  11. 2020Text-Based Ideal Pointscited by 22
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Citing papers per year: 2023: 2; 2024: 2; 2025: 11; 2026: 3.

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  5. 2025Interpretable Text Embeddings and Text Similarity Explanation: A Surveycited by 12
  6. 2025Conversational User-AI Intervention: A Study on Prompt Rewriting for Improved LLM Response Generationcited by 10
  7. 2025Position: Text Embeddings Should Capture Implicit Semantics, Not Just Surface Meaningcited by 10
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  9. 2023"We Demand Justice!": Towards Social Context Grounding of Political Textscited by 4
  10. 2025Understanding Common Ground Misalignment in Goal-Oriented Dialog: A Case-Study with Ubuntu Chat Logscited by 4
  11. 2025Talking Point based Ideological Discourse Analysis in News Eventscited by 4
  12. 2025Structuring the Unstructured: A Systematic Review of Text-to-Structure Generation for Agentic AI with a Universal Evaluation Frameworkcited by 2
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  4. 2026Learning to Diagnose and Correct Moral Errors: Beyond Shallow Heuristics in Moral Alignmentcited by 0
  5. 2026Large Language Model and Formal Concept Analysis: a comparative study for Topic Modelingcited by 0
  6. 2026Scene Abstraction for Lexical Semantics: Structured Representations of Situated Meaningcited by 0