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Current automated fact-checking (AFC) approaches commonly evaluate evidence either implicitly via the predicted verdicts or by comparing retrieved evidence with a predefined closed knowledge source, such as Wikipedia. However, these methods suffer fr
Externí odkaz:
http://arxiv.org/abs/2411.05375
Autor:
Aly, Rami, Vlachos, Andreas
Fact verification on tabular evidence incentivises the use of symbolic reasoning models where a logical form is constructed (e.g. a LISP-style program), providing greater verifiability than fully neural approaches. However, these systems typically re
Externí odkaz:
http://arxiv.org/abs/2411.01093
Autor:
Schlichtkrull, Michael, Chen, Yulong, Whitehouse, Chenxi, Deng, Zhenyun, Akhtar, Mubashara, Aly, Rami, Guo, Zhijiang, Christodoulopoulos, Christos, Cocarascu, Oana, Mittal, Arpit, Thorne, James, Vlachos, Andreas
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. Evidence can be found either via a search engine, or via a knowledge store
Externí odkaz:
http://arxiv.org/abs/2410.23850
The conformity effect describes the tendency of individuals to align their responses with the majority. Studying this bias in large language models (LLMs) is crucial, as LLMs are increasingly used in various information-seeking and decision-making ta
Externí odkaz:
http://arxiv.org/abs/2410.12428
Active Learning aims to minimize annotation effort by selecting the most useful instances from a pool of unlabeled data. However, typical active learning methods overlook the presence of distinct example groups within a class, whose prevalence may va
Externí odkaz:
http://arxiv.org/abs/2410.08972
The recent development of fact verification systems with natural logic has enhanced their explainability by aligning claims with evidence through set-theoretic operators, providing faithful justifications. Despite these advancements, such systems oft
Externí odkaz:
http://arxiv.org/abs/2410.03341
In this paper, we consider the problem of predicting unknown targets from data. We propose Online Residual Learning (ORL), a method that combines online adaptation with offline-trained predictions. At a lower level, we employ multiple offline predict
Externí odkaz:
http://arxiv.org/abs/2409.04069
Quantum sensors promise revolutionary advances in medical imaging, energy production, mass detection, geodesy, foundational physics research, and a host of other fields. In many sensors, the signal takes the form of a changing qubit frequency, which
Externí odkaz:
http://arxiv.org/abs/2408.15926
We leverage generative large language models for language learning applications, focusing on estimating the difficulty of foreign language texts and simplifying them to lower difficulty levels. We frame both tasks as prediction problems and develop a
Externí odkaz:
http://arxiv.org/abs/2407.18061
Non-invasive flow measurement techniques, such as particle tracking velocimetry, resolve 3D velocity fields by pairing tracer particle positions in successive time steps. These trajectories are crucial for evaluating physical quantities like vorticit
Externí odkaz:
http://arxiv.org/abs/2407.04583