Penguins Don’t Fly: Reasoning about Generics through Instantiations and Exceptions (2023.eacl-main)
Copied to clipboard
| Challenge: | Generics express generalizations about the world that are not universally true . commonsense knowledge bases encode some generic knowledge but rarely enumerate exceptions . |
| Approach: | They propose a framework informed by linguistic theory to generate exemplars for generics . they generate 19k exemplar cases for 650 generics and show they outperform a strong baseline . |
| Outcome: | The proposed framework outperforms a baseline framework by 12.8 precision points. |
Similar Papers
Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing (D19-60)
Copied to clipboard
| Challenge: | Workshop on Commonsense Inference in Natural Language Processing focuses on commonsense knowledge representation and application in NLP tasks. |
| Approach: | COIN is a workshop on commonsense inference in natural language processing . workshop included two shared tasks on reading comprehension using commonsensense knowledge . |
| Outcome: | the workshop focused on modeling commonsense knowledge and commonsensing in natural language processing tasks. |
Are LLMs classical or nonmonotonic reasoners? Lessons from generics (2024.acl-short)
Copied to clipboard
| Challenge: | Recent research on nonmonotonic reasoning has provided evidence of impressive performance and flexible adaptation to machine generated or human critique. |
| Approach: | They propose to use generics to explain why birds fly and exceptions such as penguins don't fly to maintain stable beliefs on truth conditions of generics. |
| Outcome: | The proposed task features generics, such as ‘Birds fly’, and exceptions, ‘Penguins don’t fly’. |
Lawyers are Dishonest? Quantifying Representational Harms in Commonsense Knowledge Resources (2021.emnlp-main)
Copied to clipboard
| Challenge: | Commonsense knowledge bases are mostly human-generated and reflect societal biases . a filtering-based approach can reduce the issues in both resources and models but leads to a performance drop . |
| Approach: | They propose a filtering-based approach to mitigating representational harms in ConceptNet and GenericsKB . they propose filtered-based approaches can reduce issues in both resources and models but leads to performance drop . |
| Outcome: | The proposed approach reduces issues in resources and models but leads to performance drop . the paper proposes a filtering-based approach that reduces biases but leaves room for future work . |
Generics are not quantificational: A new path from language models to semantic theory (2026.findings-acl)
Copied to clipboard
| Challenge: | Generic sentences express generalizations that tolerate exceptions without explicitly communicating information about quantities. |
| Approach: | They compare generics and quantificational sentences to find out what quantifiers are . they argue that generics are not quantificationals, contrary to dominant views . |
| Outcome: | The proposed model recovers many semantic facts about quantifiers and their "quantificational counterparts". |
CLIX: Cross-Lingual Explanations of Idiomatic Expressions (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing definition generation systems are difficult to use in second language learning due to the presence of unfamiliar words and grammar. |
| Approach: | They propose to use cross-lingual explanations of idiomatic expressions to support vocabulary expansion for language learners. |
| Outcome: | The proposed system is able to explain idiomatic expressions in non-standard language. |
Natural Language Deduction with Incomplete Information (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing systems for reasoning given incomplete information are inadequate . current approaches to reasoning are based on latent reasoning by large language models . |
| Approach: | They propose a system that generates a natural language "proof" by abductively inferring a premise from another premise and a conclusion. |
| Outcome: | The proposed system can handle the underspecified setting where not all premises are stated at the outset; additional assumptions need to be materialized to prove a claim. |
Commonsense Reasoning for Natural Language Processing (2020.acl-tutorials)
Copied to clipboard
| Challenge: | In this tutorial, we will outline the various types of commonsense knowledge and discuss techniques to gather and represent commonsence knowledge. |
| Approach: | This tutorial will provide researchers with the critical foundations and recent advances in commonsense representation and reasoning. |
| Outcome: | This tutorial will outline the various types of commonsense and discuss techniques to gather and represent commonsence knowledge while highlighting the challenges specific to this type of knowledge (e.g., reporting bias). |
Generic Overgeneralization in Pre-trained Language Models (2022.coling-1)
Copied to clipboard
| Challenge: | Generic statements such as "ducks lay eggs" are perceived as false universally . however, universally quantified statements such "all tigers have stripes" should be perceived as true . |
| Approach: | They investigate the generic overgeneralization effect in pre-trained language models . they show that pre-trainers tend to treat quantified generic statements as if they were true . |
| Outcome: | The proposed model reduces, but does not eliminate, generic overgeneralization bias . the model can be used to inject factual knowledge about kinds into pre-trained models . |
Generics are puzzling. Can language models find the missing piece? (2025.coling-main)
Copied to clipboard
| Challenge: | Generic sentences express generalisations about the world without explicit quantification . human biases in stereotypes can be observed in language models, authors say . |
| Approach: | They analyze generic sentences to determine their quantification and quantify their implicit quantifications using language models. |
| Outcome: | The proposed model shows that generics are more context-sensitive than determiner quantifiers and express weak generalisations. |
Uncovering Probabilistic Implications in Typological Knowledge Bases (P19-1)
Copied to clipboard
| Challenge: | linguistic typology is concerned with mapping out the relationships between languages with structural and functional properties. |
| Approach: | They propose a computational model which identifies known and new linguistic universals and uncovers them worthy of further linguistic investigation. |
| Outcome: | The proposed model outperforms baselines and knowledge base baselines. |