Papers with abstract representations
Document Representation Learning for Patient History Visualization (C18-2)
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| Challenge: | In medicine, selecting relevant reports from a large database is time-consuming and could result in overlooking important information. |
| Approach: | They propose a representation learning algorithm that creates a semantic representation space for documents where the clinically related documents lie close to each other. |
| Outcome: | The proposed model can be used to generate a diagrammatic summary of a set of documents each of which pertains to loosely related topics. |
ABEX: Data Augmentation for Low-Resource NLU via Expanding Abstract Descriptions (2024.acl-long)
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Sreyan Ghosh, Utkarsh Tyagi, Sonal Kumar, Chandra Kiran Evuru, Ramaneswaran S, S Sakshi, Dinesh Manocha
| Challenge: | ABEX is a novel and effective generative data augmentation methodology for low-resource Natural Language Understanding (NLU) tasks. |
| Approach: | They propose a novel generative data augmentation methodology for low-resource Natural Language Understanding (NLU) tasks based on a paradigm for generating diverse forms of an input document . |
| Outcome: | The proposed method outperforms all baselines qualitatively with improvements of 0.04% - 38.8%. |
The Role of Abstract Representations and Observed Preferences in the Ordering of Binomials in Large Language Models (2025.acl-short)
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| Challenge: | Using binomial ordering preferences, large language models learn abstract representations versus more superficial aspects of their training corpora. |
| Approach: | They examine binomial ordering preferences involving two conjoined nouns in English and examine whether large language models rely on observed binomialisms or on abstract ordering preferences. |
| Outcome: | The proposed model learning is based on the observed binomial ordering preferences in English, and not on human linguis-tic input. |
Improve Rule Retrieval and Reasoning with Self-Induction and Relevance ReEstimate (2025.findings-acl)
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| Challenge: | Existing rule retrieval methods suffer from low accuracy due to semantic gap between instantiated facts and abstract representations of rules. |
| Approach: | They propose a method that induces inferential rules that might offer benefits for reasoning by abstracting the underlying knowledge and logical structure in queries. |
| Outcome: | The proposed method improves retrieval effectiveness and accuracy across settings. |
Large Language Models Badly Generalize across Option Length, Problem Types, and Irrelevant Noun Replacements (2025.emnlp-main)
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| Challenge: | Existing benchmarks have exposed patterns and may not truly assess generalization ability of Large Language Models (LLMs). |
| Approach: | They propose a “Generalization Stress Test” to assess Large Language Models’ generalization ability under slight and controlled perturbations, including option length, problem types, and irrelevant noun replacements. |
| Outcome: | The proposed test shows that LLMs exhibit severe accuracy drops and unexpected biases when faced with minor but content-preserving modifications. |
Mapping semantic networks to Dutch word embeddings as a diagnostic tool for cognitive decline (2025.emnlp-main)
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| Challenge: | Semantic networks are abstract representations of the semantic memory system and can be used to estimate networks . |
| Approach: | They used Dutch verbal fluency data to explore the relationship between semantic networks and cognitive health. |
| Outcome: | The proposed measures predict cognitive health scores on the Mini-Mental State Examination (MMSE) while the traditional number-of-words measure was not significant, the results suggest that semantic network metrics may provide a more sensitive measure of cognitive health than traditional scoring. |