LEGO: Latent Execution-Guided Reasoning for Multi-Hop Question Answering on Knowledge Graphs
By Hongyu Ren, Hanjun Dai, Bo Dai, Xinyun Chen, Michihiro Yasunaga, Haitian Sun, Dale Schuurmans, Dale Schuurmans, Jure Leskovec, Denny Zhou. 2021
96 citations recorded by the source corpus.
Subfields: Retrieval-Augmented Generation, Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Retrieval-Augmented Generation
- Luke S. Zettlemoyer
- Weizhu Chen
- Noah A. Smith
- Christopher D. Manning
- Percy Liang
- Caiming Xiong
- Victor Zhong
- Wenhu Chen
In Natural Language Processing
- Percy Liang
- Caiming Xiong
- Luke S. Zettlemoyer
- Graham Neubig
- Richard Socher
- Jonathan Berant
- Mohit Bansal
- Noah A. Smith
Citations in the corpus
- Reasoning Like Program Executors (2022, background): “, 2021), knowledge base question answering (Ren et al., 2021) and 3D rendering (Tian et al.”
- UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models (2022, methodology): “, semantic parsing, use customized decoders to generate programs (Yin and Neubig, 2018; Ren et al., 2021).”
- Large Language Models as Analogical Reasoners (2023, background): “…et al., 2019; Feng et al., 2020; Zhang et al., 2022a; Yasunaga et al., 2021; 2022a; Xie et al., 2022), and incorporating external modules for reasoning such as logic and program execution (Chen et al., 2018; 2019; Yasunaga & Liang, 2020; Gupta et al., 2020; Ren et al., 2021; Zhang et al., 2023).”
- Deep Bidirectional Language-Knowledge Graph Pretraining (2022, unknown): “KGs offer structured background knowledge by representing entities as nodes and Accepted to Workshop on Knowledge Augmented Methods for Natural Language Processing, in conjunction with AAAI 2023. relations between them as edges, and also offer scaffolds for structured, multi-step reasoning about…”
- GreaseLM: Graph REASoning Enhanced Language Models for Question Answering (2022, background): “Previous research has demonstrated the significant role KGs can play in structured reasoning and query answering (Ren et al., 2020; 2021; Ren & Leskovec, 2020).”