| Challenge: | generating and publishing content is so easy, we are bombarded with information and are exposed to all kinds of claims. |
| Approach: | They propose a formal definition of provenance graph for a given natural language claim . they evaluate the approach using two benchmark datasets to capture provenance . |
| Outcome: | The proposed method shows initial success in capturing provenance and its effectiveness on claim verification. |
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| Challenge: | Several studies have identified such linguistic classes of words that occur frequently in natural language text and are bias-inducing by virtue of their framing effects. |
| Approach: | They propose to use linguistic cues to induce subtle biases through implied sentiment and presupposed facts to influence the distribution of the generated text. |
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ClaimVer: Explainable Claim-Level Verification and Evidence Attribution of Text Through Knowledge Graphs (2024.findings-emnlp)
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Preetam Prabhu Srikar Dammu, Himanshu Naidu, Mouly Dewan, YoungMin Kim, Tanya Roosta, Aman Chadha, Chirag Shah
| Challenge: | Despite the fact that many fact-checking tools lack granularity and explainability, they lack the ability to be useful in various contexts. |
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Inspecting the concept knowledge graph encoded by modern language models (2021.findings-acl)
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| Challenge: | Pre-trained language models are used to solve tasks such as summarization and information retrieval. |
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What is Your Article Based On? Inferring Fine-grained Provenance (2021.acl-long)
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| Challenge: | a new study of claim provenance seeks to trace and explain the origins of claims . a critical reader must be able to assess where the information comes from and where it originates from . |
| Approach: | They propose a method to model and reason about the provenance of multiple interacting claims . they propose generating metadata for the source article based on context and search signals . |
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A Graph per Persona: Reasoning about Subjective Natural Language Descriptions (2024.findings-acl)
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| Challenge: | Existing large language models (LLMs) perform poorly in reasoning about subjective knowledge, showing strong biases and lack interpretability requirements. |
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Natural Language Deduction with Incomplete Information (2022.emnlp-main)
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| 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. |
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Annotation Artifacts in Natural Language Inference Data (N18-2)
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| Challenge: | Large-scale datasets for natural language inference are created by crowdsourcing annotations . authors show that success of natural language models to date has been overestimated . |
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Claim Verification in the Age of Large Language Models: A Survey (2026.acl-srw)
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| Challenge: | Recent election cycles have seen a large number of false information spread across social media and news platforms. |
| Approach: | They propose a framework for automated claim verification using Large Language Models and Retrieval Augmented Generation. |
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Towards Effective Extraction and Evaluation of Factual Claims (2025.acl-long)
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| Challenge: | Lack of a standardized evaluation framework impedes assessment and comparison of claim extraction methods. |
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Figurative Language in Recognizing Textual Entailment (2021.findings-acl)
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| Challenge: | Existing RTE models struggle to capture figurative language, despite its ubiquity, it remains a bottleneck in automatic text understanding. |
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