MACE deploys three specialized LLM agents (Planner, Executor, Verifier) with zero-shot CoT to verify claims from tables, matching SOTA performance on two datasets and near-SOTA on two others using models 2-8x smaller than prior bests.
InFind- ings of the Association for Computational Linguistics: NAACL 2022, pages 1–16
2 Pith papers cite this work. Polarity classification is still indexing.
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Zero-shot LLMs exhibit intervention bias in educational advising, over-recommending actions by 43 percentage points, while supervised DT and XGBoost models achieve near-zero calibration error and macro-F1 of 0.79.
citing papers explorer
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A Multi-Agent Approach for Claim Verification from Tabular Data Documents
MACE deploys three specialized LLM agents (Planner, Executor, Verifier) with zero-shot CoT to verify claims from tables, matching SOTA performance on two datasets and near-SOTA on two others using models 2-8x smaller than prior bests.
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Deterministic Decisions for High-Stakes AI. A Zero-Egress Pipeline with the Deployability of RAG and the Accuracy of Machine Learning
Zero-shot LLMs exhibit intervention bias in educational advising, over-recommending actions by 43 percentage points, while supervised DT and XGBoost models achieve near-zero calibration error and macro-F1 of 0.79.