JustiLM: Few-shot justification generation for explainable fact-checking of real-world claims
Justification is an explanation that supports the verdict assigned to a claim in fact-checking. However, the task of justification generation is previously oversimplified as summarization of fact-check article authored by professional checkers. In this work, we propose a realistic approach to genera...
Saved in:
Main Authors: | ZENG, Fengzhu, GAO, Wei |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2004
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/9441 https://ink.library.smu.edu.sg/context/sis_research/article/10441/viewcontent/2024.tacl_1.19_pvoa_cc_by.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
Similar Items
Similar Items
-
Prompt to be consistent is better than self-consistent? Few-shot and zero-shot fact verification with pre-trained language models
by: ZENG, Fengzhu, et al.
Published: (2023) -
Zero-shot Fact Verification by Claim Generation
by: Pan, L, et al.
Published: (2022) -
Reinforcement retrieval leveraging fine-grained feedback for fact checking news claims with Black-Box LLM
by: ZHANG, Xuan, et al.
Published: (2023) -
Explainable deep few-shot anomaly detection with deviation networks
by: PANG, Guansong, et al.
Published: (2021) -
Unlocking the capabilities of explainable few‑shot learning in remote sensing
by: Lee, Gao Yu, et al.
Published: (2024)