Papers by Xianshu Peng
GRAT: Guiding Retrieval-Augmented Reasoning through Process Rewards Tree Search (2025.acl-long)
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| Challenge: | Existing methods to enhance large models for multi-hop question-answering lack the ability for multipath exploration, strategic look-ahead, stepwise evaluation, and global selection. |
| Approach: | They propose an algorithm guided by Monte Carlo Tree Search and process rewards that assigns fine-grained rewards to each step in the search path. |
| Outcome: | The proposed algorithm outperforms various RAG-based methods on four multihop QA datasets and shows that it can self-train and self-update. |
CNEQ: Incorporating numbers into Knowledge Graph Reasoning (2024.findings-emnlp)
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| Challenge: | Complex query answering (CQA) is a task that addresses semantics of numerical entities. |
| Approach: | They propose a model that includes a Number-Entity Predictor and an Entity Filter . they use three widely-used Knowledge Graphs to perform reasoning over knowledge graphs . |
| Outcome: | The proposed model can predict entities and numerical values better than existing models . it compares or filters out entities that meet certain constraints on three widely-used Knowledge Graphs . |