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Towards Fine-Grained Reasoning for Fake News Detection

AAAI 2022 · Large language models › Language, safety and interpretability · approach: Data and hallucination detection · 79 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use FinerFact instead of KANfake news detection · PolitiFactF1: 0.8539 -> 0.9172Helped
use FinerFact instead of KANfake news detection · PolitiFactAcc: 0.8586 -> 0.9092Helped
use FinerFact instead of KANfake news detection · GossipCopF1: 0.7713 -> 0.8685Helped
use FinerFact instead of KANfake news detection · GossipCopAcc: 0.7766 -> 0.8320Helped
remove the attention prior from node-importance learning (ablation)FinerFact · PolitiFact and GossipCopmodel performanceHurt
remove the user-based reasoning channel (ablation)FinerFact · PolitiFact and GossipCopmodel performanceHurt
replace kernel-based representations with GNN-based aggregation (ablation)FinerFact · PolitiFact and GossipCopmodel performanceHurt
assign equal weight to every node instead of learning node importance (ablation)FinerFact · PolitiFact and GossipCopmodel performanceHurt
replace mutual-reinforcement evidence ranking with random evidence sampling (ablation)FinerFact · PolitiFact and GossipCopmodel performanceHurt
replace BERT with a 1-layer Transformer encoder (ablation)FinerFact · PolitiFactF1: 0.9172 -> 0.8810Hurt
replace BERT with a 1-layer Transformer encoder (ablation)FinerFact · GossipCopF1: 0.8685 -> 0.8602Hurt
use around 11 kernels for fine-grained matching (ablation)FinerFact · PolitiFact and GossipCopmodel performanceHelped

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