FC

Papers with FC

VehicleWorld: A Highly Integrated Multi-Device Environment for Intelligent Vehicle Interaction (2025.findings-emnlp)

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Jie Yang, Jiajun Chen, Zhangyue Yin, Shuo Chen, Yuxin Wang, Yiran Guo, Yuan Li, Yining Zheng, Xuanjing Huang, Xipeng Qiu

Challenge:	Traditional Function Calling (FC) approaches operate statelessly, requiring multiple exploratory calls to build environmental awareness before execution, leading to inefficiency and limited error recovery.
Approach:	They propose a state-based function call approach that maintains explicit system state awareness and implements direct state transitions to achieve target conditions.
Outcome:	The proposed approach outperforms traditional function calling approaches, achieving superior execution accuracy and reduced latency.

Fact Checking with Insufficient Evidence (2022.tacl-1)

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Pepa Atanasova, Jakob Grue Simonsen, Christina Lioma, Isabelle Augenstein

Responsible & Trustworthy NLP

Challenge:	Existing work on how to automate fact checking relies on information obtained from external sources.
Approach:	They propose a fluency-preserving method for omitting information from the evidence at the constituent and sentence level and a diagnostic dataset for FC with omitted evidence.
Outcome:	The proposed method improves evidence sufficiency prediction by 17.8 F1 score and 2.6 F1 scores.

Enhancing Factual Consistency of Abstractive Summarization (2021.naacl-main)

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Chenguang Zhu, William Hinthorn, Ruochen Xu, Qingkai Zeng, Michael Zeng, Xuedong Huang, Meng Jiang

Text Generation

Challenge:	Abstractive summarization models often distort or fabricate facts in articles . factual inconsistency is a common problem with abstractive summaries .
Approach:	They propose a fact-aware summarization model FASum to extract factual relations into the summary generation process via graph attention.
Outcome:	The proposed model can produce abstractive summaries with higher factual consistency compared with existing systems and corrects factual errors via modifying only a few keywords.

Article Reranking by Memory-Enhanced Key Sentence Matching for Detecting Previously Fact-Checked Claims (2021.acl-long)

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Qiang Sheng, Juan Cao, Xueyao Zhang, Xirong Li, Lei Zhong

Language Models

Challenge:	Existing methods to detect false claims ignore the characteristics of FC-articles . claims are often quoted to describe checked events, providing lexical information . sentence templates to introduce or debunk claims are common across articles, providing pattern information.
Approach:	They propose a model to rerank FC-articles using key sentences and pattern information.
Outcome:	The proposed model outperforms existing methods on two real-world datasets showing that key sentences can be used to predict if an article fact-checks the given claim.

ECON: On the Detection and Resolution of Evidence Conflicts (2024.emnlp-main)

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Cheng Jiayang, Chunkit Chan, Qianqian Zhuang, Lin Qiu, Tianhang Zhang, Tengxiao Liu, Yangqiu Song, Yue Zhang, Pengfei Liu, Zheng Zhang

Reasoning

Challenge:	Recent studies have shown that AI generated content is more likely to dominate search results, making it difficult to detect when compared to human-produced content.
Approach:	They propose a method for generating diverse, validated evidence conflicts to simulate real-world misinformation scenarios.
Outcome:	The proposed method enables the detection of conflicting information in real-world scenarios and shows that weaker models struggle with similar answer conflicts while stronger models show robust performance.

FC-Attack: Jailbreaking Multimodal Large Language Models via Auto-Generated Flowcharts (2025.findings-emnlp)

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Ziyi Zhang, Zhen Sun, Zongmin Zhang, Jihui Guo, Xinlei He

Responsible & Trustworthy NLP

Challenge:	Recent research shows that multimodal large language models are vulnerable to jailbreak attacks .
Approach:	They propose a jailbreak attack method based on auto-generated flowcharts . the flowchartings are then combined with a benign textual prompt to execute the attack .
Outcome:	The proposed method achieves an attack success rate of up to 96% via images and 78% via videos across multiple MLLMs.

Unknown Claims: Generation of Fact-Checking Training Examples from Unstructured and Structured Data (2024.emnlp-main)

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Jean-Flavien Bussotti, Luca Ragazzi, Giacomo Frisoni, Gianluca Moro, Paolo Papotti

Multimodality

Challenge:	Existing methods for fact-checking are labor-intensive and time-consuming.
Approach:	They propose a framework that generates training instances for FC systems automatically using textual and tabular content.
Outcome:	The proposed framework generates training instances for FC systems using textual and tabular content.

Explaining Interactions Between Text Spans (2023.emnlp-main)

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Sagnik Ray Choudhury, Pepa Atanasova, Isabelle Augenstein

Responsible & Trustworthy NLP

Challenge:	Existing highlight-based explanations focus on identifying individual important features or interactions only between adjacent tokens or tuples of tokens.
Approach:	They propose a multi-annotator dataset of human span interaction explanations for NLU and FC.
Outcome:	The proposed method compares human reasoning processes to those of a fine-tuned large language model.

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

Responsible & Trustworthy NLP

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.

GenesisFunc: Multi-Agent Data Generation for Accurate and Generalizable Function-Calling (2026.acl-long)

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Hao-Xiang Xu, Chong Deng, Jiaqing Liu, Wen Wang, Qian Chen, Lujia Bao, Xiangang Li, Zhen-Hua Ling

Challenge:	Large Language Models (LLMs) extend their capabilities through function-calling (FC) however, obtaining and annotating real function-called data is challenging, and synthetic data from existing pipelines suffers from unreliable APIs, limited tool scalability, insufficient diversity, and weak quality control.
Approach:	They propose a pipeline for generating FC training data using reliable tools and a multi-agent framework that supports a dialogue generation system that produces conversations spanning diverse scenarios.
Outcome:	The proposed pipeline outperforms open-source models in in-domain FC performance and out-of-domain generalization while reaching FC capabilities comparable to some of the latest API-based models.