DeLIVeR: Decomposed Learning for Information-grounded Veracity Recognition via Reinforced Knowledge Graph Exploration
Merged summary
TL;DR - DeLIVeR uses an RL-trained planner LLM to decompose claims into questions and strategically explore knowledge graphs for fact-checking. It improves multi-hop evidence retrieval while producing auditable verification paths.
- Trains the planner with GRPO rewards emphasizing structural diversity and verdict accuracy.
- Uses targeted question sets to retrieve high-precision evidence from structured knowledge graphs.
- With Qwen2.5-7B, achieves F1 scores of 83.73 on LIAR, 84.57 on FEVER, and 79.70 on PolitiFact.
- Reports a 10–15% improvement over HippoRAG2.
Sources (1)
DeLIVeR: Decomposed Learning for Information-grounded Veracity Recognition via Reinforced Knowledge Graph Exploration
TL;DR - DeLIVeR uses an RL-trained planner LLM to decompose claims into questions and strategically explore knowledge graphs for fact-checking. It improves multi-hop evidence retrieval while producing auditable verification paths.
- Trains the planner with GRPO rewards emphasizing structural diversity and verdict accuracy.
- Uses targeted question sets to retrieve high-precision evidence from structured knowledge graphs.
- With Qwen2.5-7B, achieves F1 scores of 83.73 on LIAR, 84.57 on FEVER, and 79.70 on PolitiFact.
- Reports a 10–15% improvement over HippoRAG2.