LFD discovers predictive text features via LLM contrastive proposals, cross-LLM Cohen's kappa screening, and residual held-out gain selection, matching baseline accuracy while achieving higher human agreement and lower label leakage on ten tasks.
BERT: Pre-training of deep bidirectional transformers for language understanding
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
A multi-agent system for explainable fake news detection that decomposes claims, retrieves evidence, verifies with calibrated confidence, and aggregates logic verdicts, showing better interpretability than BERT/RoBERTa on the LIAR benchmark despite lower raw accuracy.
TabH2O presents a unified tabular foundation model with dual-head architecture and single-stage pretraining that achieves an average rank of 2.55 on the TALENT benchmark, outperforming several established methods.
citing papers explorer
-
Interpretable Discriminative Text Representations via Agreement and Label Disentanglement
LFD discovers predictive text features via LLM contrastive proposals, cross-LLM Cohen's kappa screening, and residual held-out gain selection, matching baseline accuracy while achieving higher human agreement and lower label leakage on ten tasks.
-
TRUST Agents: A Collaborative Multi-Agent Framework for Fake News Detection, Explainable Verification, and Logic-Aware Claim Reasoning
A multi-agent system for explainable fake news detection that decomposes claims, retrieves evidence, verifies with calibrated confidence, and aggregates logic verdicts, showing better interpretability than BERT/RoBERTa on the LIAR benchmark despite lower raw accuracy.
-
TabH2O: A Unified Foundation Model for Tabular Prediction
TabH2O presents a unified tabular foundation model with dual-head architecture and single-stage pretraining that achieves an average rank of 2.55 on the TALENT benchmark, outperforming several established methods.