Summary of Maple: Micro Analysis Of Pairwise Language Evolution For Few-shot Claim Verification, by Xia Zeng et al.
MAPLE: Micro Analysis of Pairwise Language Evolution for Few-Shot Claim Verification
by Xia Zeng, Arkaitz Zubiaga
First submitted to arxiv on: 29 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research proposes MAPLE, a novel approach for few-shot claim verification in automated fact-checking. The model uses a small seq2seq architecture and a semantic measure to analyze the alignment between claims and evidence, leveraging unlabelled pairwise data to facilitate verification with low computational demands. MAPLE outperforms state-of-the-art baselines SEED, PET, and LLaMA 2 on three datasets: FEVER, Climate FEVER, and SciFact. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Claim verification is a crucial step in fact-checking that assesses the truthfulness of claims against evidence. This study explores few-shot claim verification using only limited data for supervision. MAPLE uses a small seq2seq model and a novel semantic measure to analyze the alignment between claims and evidence, leveraging unlabelled pairwise data to facilitate verification with low computational demands. The results show that MAPLE performs better than state-of-the-art models on three datasets. |
Keywords
* Artificial intelligence * Alignment * Few shot * Llama * Seq2seq
