Papers with Claim verification
MAPLE: Micro Analysis of Pairwise Language Evolution for Few-Shot Claim Verification (2024.findings-eacl)
Copied to clipboard
| Challenge: | Existing methods for verification of claims are limited by the availability of labeled data. |
| Approach: | They propose a method that explores the alignment between a claim and its evidence using a seq2seq model and a novel semantic measure. |
| Outcome: | The proposed method shows significant performance improvements over baselines SEED, PET and LLaMA 2 across three fact-checking datasets. |
SYNTHVERIFY: Enhancing Zero-Shot Claim Verification through Step-by-Step Synthetic Data Generation (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for claim verification are inefficient or rely on external documents. |
| Approach: | They propose a step-by-step prompting-based synthetic data generation framework to enhance zero-shot claim verification. |
| Outcome: | The proposed framework bridges LLMs’ knowledge gaps in specialized domains without access to external corpora or sacrificing generalizability. |
Sentence-Level Evidence Embedding for Claim Verification with Hierarchical Attention Networks (P19-1)
Copied to clipboard
| Challenge: | Claim verification is cumbersome and inefficient for human fact-checkers to find consistent pieces of evidence. |
| Approach: | They propose an end-to-end hierarchical attention network that learns to represent coherent evidence and their semantic relatedness with the claim. |
| Outcome: | The proposed model outperforms state-of-the-art models on three datasets . it is based on a coherence-based attention layer and entailment-based one . |
Verify-in-the-Graph: Entity Disambiguation Enhancement for Complex Claim Verification with Interactive Graph Representation (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing approaches to claim verification are based on decomposing claims into sub-claims and querying a knowledge base to resolve hidden or ambiguous entities. |
| Approach: | They propose a framework that leverages the reasoning and comprehension abilities of LLM agents to solve ambiguous entities in a graph. |
| Outcome: | The proposed framework achieves competitive performance compared to baselines across benchmarks. |
KG-CRAFT: Knowledge Graph-based Contrastive Reasoning with LLMs for Enhancing Automated Fact-checking (2026.eacl-long)
Copied to clipboard
| Challenge: | Claim verification is a core module in automated fact-checking systems, tasked with determining claim veracity using retrieved evidence. |
| Approach: | They propose a knowledge graph-based contrastive reasoning method that constructs a graph from claims and associated reports and formulates contextually relevant contrastive questions based on the knowledge graph structure. |
| Outcome: | The proposed method improves accuracy on two real-world datasets and is compared with existing methods. |
Explainable Claim Verification via Knowledge-Grounded Reasoning with Large Language Models (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing claims verification models rely on annotated data, which is expensive to create at a large scale. |
| Approach: | They propose a model that can verify complex claims without annotated data . they leverage the in-context learning ability of Large Language Models to translate a claim into a First-Order-Logic clause . |
| Outcome: | The proposed model outperforms baseline models on three datasets . it performs well on the datasets, and the results are published online. |
Veri-R1: Toward Precise and Faithful Claim Verification via Online Reinforcement Learning (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to online claim verification rely on prompt engineering or pre-designed reasoning workflows. |
| Approach: | They propose an online reinforcement learning framework that enables an LLM to interact with a search engine and receive reward signals that explicitly shape its planning, retrieval, and reasoning behaviors. |
| Outcome: | Empirical results show that Veri-R1 improves joint accuracy by 30% and doubles evidence score, often surpassing larger-scale model counterparts. |