Evaluating adversarial attacks against multiple fact verification systems (D19-1)
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| Challenge: | Automated fact verification is progressing due to advances in modeling and availability of large datasets. |
| Approach: | They propose two scoring metrics which take into account the correctness of adversarial instances. |
| Outcome: | The proposed method and paraphrasing method have higher potency and higher resilience than baselines. |
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Adversarial Attacks Against Automated Fact-Checking: A Survey (2025.emnlp-main)
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Fanzhen Liu, Sharif Abuadbba, Kristen Moore, Surya Nepal, Cecile Paris, Jia Wu, Jian Yang, Quan Z. Sheng
| Challenge: | Existing fact-checking systems are vulnerable to adversarial attacks that manipulate or generate claims, evidence, or claim-evidence pairs. |
| Approach: | They examine the impact of adversarial attacks on existing AFC systems and examine their impact on existing ones. |
| Outcome: | The findings highlight the need for resilient fact-checking frameworks in limiting misinformation spread and supporting public trust. |
DeSePtion: Dual Sequence Prediction and Adversarial Examples for Improved Fact-Checking (2020.acl-main)
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Christopher Hidey, Tuhin Chakrabarty, Tariq Alhindi, Siddharth Varia, Kriste Krstovski, Mona Diab, Smaranda Muresan
| Challenge: | Fact Extraction and Verification datasets provide a resource for end-to-end fact-checking, requiring retrieval of evidence from Wikipedia to validate a veracity prediction. |
| Approach: | They propose a system that is resilient to attacks by multiple propositions, temporal reasoning, ambiguity and lexical variation and a sequence of evidence sentences and veracity relation predictions. |
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The FEVER2.0 Shared Task (D19-66)
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| Challenge: | Existing deep neural models are becoming more complex and difficult to understand and characterize their behaviour. |
| Approach: | They present the results of the second Fact Extraction and VERification (FEVER2.0) Shared Task. |
| Outcome: | The proposed task was based on the second Fact Extraction and VERification (FEVER2.0) shared task. |
How Robust are Fact Checking Systems on Colloquial Claims? (2021.naacl-main)
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| Challenge: | Existing fact checking systems that perform well on colloquial claims significantly degenerate on collotic claims with the same semantics. |
| Approach: | They propose to transfer the styles of claims from FEVER into colloquialism to investigate fact checking systems on colloqual claims. |
| Outcome: | The proposed system significantly degenerates on colloquial claims with the same semantics. |
FactEval: Evaluating the Robustness of Fact Verification Systems in the Era of Large Language Models (2025.naacl-long)
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| Challenge: | Large language models (LLMs) have made significant advances in every natural language processing task, but they are vulnerable to small perturbations in the inputs, raising concerns about their robustness in the real world. |
| Approach: | They propose a large-scale benchmark for extensive evaluation of LLMs in the fact verification domain covering 17 realistic word-level and character-level perturbations and 4 types of subpopulations. |
| Outcome: | The proposed model is brittle to small input changes and exhibits performance variations across different subpopulations. |
A Closer Look into the Robustness of Neural Dependency Parsers Using Better Adversarial Examples (2021.findings-acl)
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| Challenge: | Neural network-based models have been successful in a wide range of NLP tasks, but their performance is undermined by adversarial examples that would pose no confusion for humans. |
| Approach: | They propose a method to generate high-quality adversarial examples with a higher number of candidate generators and stricter filters and then verify their quality using automatic and human evaluations. |
| Outcome: | The proposed method improves the robustness of English parsing models by relying on adversarial training and model ensembling. |
FEVER: a Large-scale Dataset for Fact Extraction and VERification (N18-1)
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| Challenge: | 185,445 claims generated by altering sentences from Wikipedia are verified without knowledge of the sentence they were derived from. |
| Approach: | They propose a publicly available dataset for verification against textual sources, FEVER: Fact Extraction and VERification. |
| Outcome: | The proposed dataset achieves 31.87% accuracy on labeling a claim accompanied by the correct evidence, compared to 50.91% if we ignore the evidence. |
FEVER Breaker’s Run of Team NbAuzDrLqg (D19-66)
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| Challenge: | In the second workshop on Fact Extraction and VERification, the goal is to develop a fact-check system which can resolve "fake news" and misinformation problems. |
| Approach: | They propose to use a model to retrieve evidence when appropriate query terms could not be easily generated from the claim. |
| Outcome: | The proposed models were able to get both the evidence and label correct in 20% of the data. |
Are Factuality Checkers Reliable? Adversarial Meta-evaluation of Factuality in Summarization (2021.findings-emnlp)
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| Challenge: | Despite the progress of factual evaluation methods, they are limited in their opacity and lack the ability to assess the factuality of the summaries. |
| Approach: | They propose to use a meta-evaluation methodology to diagnose the fine-grained strengths and weaknesses of 6 existing top-performing metrics over 24 diagnostic test datasets. |
| Outcome: | The proposed method diagnoses the strengths and weaknesses of 6 existing top-performing metrics over 24 diagnostic test datasets and searches for directions for further improvement by data augmentation. |
Evaluating the Validity of Word-level Adversarial Attacks with Large Language Models (2024.findings-acl)
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| Challenge: | Existing adversarial examples can generate invalid adversarials due to significant changes in semantic meanings compared to their originals. |
| Approach: | They propose to use a large language model to evaluate adversarial examples by semantic constraints. |
| Outcome: | The proposed method can generate valid adversarial examples even when they are not equipped with semantic constraints. |