83 citations · 316 across the 22 of their papers we have counts for
24 papers
MEDIAREF: A Public Knowledge Store for Media Background Checks
Benjamin Nichols, Michael Schlichtkrull, Nedjma Ousidhoum
LLM-based retrieval-augmented generation (RAG) is increasingly used for automated fact-checking (AFC) and related tasks. By grounding LLM outputs in retrieved evidence, RAG-based s…
Reference-Free Evaluation of Reasoning in Open-Ended Question Answering
Guneet Singh Kohli, Yuxiang Zhou, Michael Sejr Schlichtkrull +2
AI-generated answers in high-stakes domains are often fluent but difficult to verify, especially when they contain multi-step reasoning rather than a single final answer. We propos…
IYKYK: Using language models to decode extremist cryptolects
Christine de Kock, Arij Riabi, Zeerak Talat +3
Extremist groups develop complex in-group language, also referred to as cryptolects, to exclude or mislead outsiders. We investigate the ability of current language technologies to…
Social Good or Scientific Curiosity? Uncovering the Research Framing Behind NLP Artefacts
Eric Chamoun, Nedjma Ousidhoum, Michael Schlichtkrull +1
Clarifying the research framing of NLP artefacts (e.g., models, datasets, etc.) is crucial to aligning research with practical applications. Recent studies manually analyzed NLP re…
AVerImaTeC: A Dataset for Automatic Verification of Image-Text Claims with Evidence from the Web
Rui Cao, Zifeng Ding, Zhijiang Guo +2
Textual claims are often accompanied by images to enhance their credibility and spread on social media, but this also raises concerns about the spread of misinformation. Existing d…
Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs
Rui Cao, Yuming Jiang, Michael Schlichtkrull +1
Multimodal Large Language Models (MLLMs) can enhance trustworthiness by aligning with human preferences. As human preference labeling is laborious, recent works employ evaluation m…
Ev2R: Evaluating Evidence Retrieval in Automated Fact-Checking
Mubashara Akhtar, Michael Schlichtkrull, Andreas Vlachos
Current automated fact-checking (AFC) approaches typically evaluate evidence either implicitly via the predicted verdicts or through exact matches with predefined closed knowledge…
The Automated Verification of Textual Claims (AVeriTeC) Shared Task
Michael Schlichtkrull, Yulong Chen, Chenxi Whitehouse +9
The Automated Verification of Textual Claims (AVeriTeC) shared task asks participants to retrieve evidence and predict veracity for real-world claims checked by fact-checkers. Evid…
Generating Media Background Checks for Automated Source Critical Reasoning
Michael Schlichtkrull
Not everything on the internet is true. This unfortunate fact requires both humans and models to perform complex reasoning about credibility when working with retrieved information…
Document-level Claim Extraction and Decontextualisation for Fact-Checking
Zhenyun Deng, Michael Schlichtkrull, Andreas Vlachos
Selecting which claims to check is a time-consuming task for human fact-checkers, especially from documents consisting of multiple sentences and containing multiple claims. However…
Automated Focused Feedback Generation for Scientific Writing Assistance
Eric Chamoun, Michael Schlichktrull, Andreas Vlachos
Scientific writing is a challenging task, particularly for novice researchers who often rely on feedback from experienced peers. Recent work has primarily focused on improving surf…
Multimodal Automated Fact-Checking: A Survey
Mubashara Akhtar, Michael Schlichtkrull, Zhijiang Guo +3
Misinformation is often conveyed in multiple modalities, e.g. a miscaptioned image. Multimodal misinformation is perceived as more credible by humans, and spreads faster than its t…
AVeriTeC: A Dataset for Real-world Claim Verification with Evidence from the Web
Michael Schlichtkrull, Zhijiang Guo, Andreas Vlachos
Existing datasets for automated fact-checking have substantial limitations, such as relying on artificial claims, lacking annotations for evidence and intermediate reasoning, or in…
The Intended Uses of Automated Fact-Checking Artefacts: Why, How and Who
Michael Schlichtkrull, Nedjma Ousidhoum, Andreas Vlachos
Automated fact-checking is often presented as an epistemic tool that fact-checkers, social media consumers, and other stakeholders can use to fight misinformation. Nevertheless, fe…
A Survey on Automated Fact-Checking
Zhijiang Guo, Michael Schlichtkrull, Andreas Vlachos
Fact-checking has become increasingly important due to the speed with which both information and misinformation can spread in the modern media ecosystem. Therefore, researchers hav…
FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured information
Rami Aly, Zhijiang Guo, Michael Schlichtkrull +5
Fact verification has attracted a lot of attention in the machine learning and natural language processing communities, as it is one of the key methods for detecting misinformation…
NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned
Sewon Min, Jordan Boyd-Graber, Chris Alberti +50
We review the EfficientQA competition from NeurIPS 2020. The competition focused on open-domain question answering (QA), where systems take natural language questions as input and…
Joint Verification and Reranking for Open Fact Checking Over Tables
Michael Schlichtkrull, Vladimir Karpukhin, Barlas Oğuz +3
Structured information is an important knowledge source for automatic verification of factual claims. Nevertheless, the majority of existing research into this task has focused on…
UniK-QA: Unified Representations of Structured and Unstructured Knowledge for Open-Domain Question Answering
Barlas Oguz, Xilun Chen, Vladimir Karpukhin +6
We study open-domain question answering with structured, unstructured and semi-structured knowledge sources, including text, tables, lists and knowledge bases. Departing from prior…
Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking
Michael Sejr Schlichtkrull, Nicola De Cao, Ivan Titov
Graph neural networks (GNNs) have become a popular approach to integrating structural inductive biases into NLP models. However, there has been little work on interpreting them, an…
Evaluating for Diversity in Question Generation over Text
Michael Sejr Schlichtkrull, Weiwei Cheng
Generating diverse and relevant questions over text is a task with widespread applications. We argue that commonly-used evaluation metrics such as BLEU and METEOR are not suitable…
How do Decisions Emerge across Layers in Neural Models? Interpretation with Differentiable Masking
Nicola De Cao, Michael Schlichtkrull, Wilker Aziz +1
Attribution methods assess the contribution of inputs to the model prediction. One way to do so is erasure: a subset of inputs is considered irrelevant if it can be removed without…
Modeling Relational Data with Graph Convolutional Networks
Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem +3
Knowledge graphs enable a wide variety of applications, including question answering and information retrieval. Despite the great effort invested in their creation and maintenance,…
Cross-Lingual Dependency Parsing with Late Decoding for Truly Low-Resource Languages
Michael Sejr Schlichtkrull, Anders Søgaard
In cross-lingual dependency annotation projection, information is often lost during transfer because of early decoding. We present an end-to-end graph-based neural network dependen…