Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation | Paper Citation Record

Observation 87fb2537-209e-4249-bf2a-2a5eed02c4e0 · outbound

This paper cites A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity.

Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity

Reference 1

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This paper cites Freebase: a collaboratively created graph database for structuring human knowledge.

Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation Freebase: a collaboratively created graph database for structuring human knowledge

Reference 2

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This paper cites B., Lespiau, J.-B., Damoc, B., Clark, A., et al.

Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation B., Lespiau, J.-B., Damoc, B., Clark, A., et al

Reference 3

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This paper cites D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.

Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al

Reference 4

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This paper cites and Mugnier, M.-L.

Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation and Mugnier, M.-L

Reference 5

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Observation 4b161226-ea87-4555-b370-4e9d2bb97355 · outbound

This paper cites Pathrag: Pruning graph-based retrieval augmented generation with relational paths.

Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation Pathrag: Pruning graph-based retrieval augmented generation with relational paths

Reference 6

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This paper cites Benchmarking large language models in retrieval-augmented generation.

Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation Benchmarking large language models in retrieval-augmented generation

Reference 7

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Observation 5c322be9-b2ec-4cc2-8310-145178fed173 · outbound

This paper cites Premise Order Matters in Reasoning with Large Language Models.

Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation Premise Order Matters in Reasoning with Large Language Models

Reference 8

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This paper cites Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs.

Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs

Reference 9

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This paper cites Principal neighbourhood aggregation for graph nets.

Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation Principal neighbourhood aggregation for graph nets

Reference 10

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Observation 4b2844c2-6dd0-40ee-8221-fc01d0a30cef · outbound

This paper cites R., Eisenschlos, J.

Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation R., Eisenschlos, J

Reference 11

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Observation f28e52d0-0906-4f6f-820b-f5cf5ccb4582 · outbound

This paper cites From Local to Global: A Graph RAG Approach to Query-Focused Summarization.

Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation From Local to Global: A Graph RAG Approach to Query-Focused Summarization

Reference 12

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Observation 971171bd-2943-4b94-971d-b2dceaa9c964 · outbound

This paper cites A survey on rag meeting llms: Towards retrieval-augmented large language models.

Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation A survey on rag meeting llms: Towards retrieval-augmented large language models

Reference 13

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This paper cites Efficient reasoning models: A survey.

Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation Efficient reasoning models: A survey

Reference 14

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This paper cites Retrieval-Augmented Generation for Large Language Models: A Survey.

Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation Retrieval-Augmented Generation for Large Language Models: A Survey

Reference 15

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This paper cites Beyond iid: three levels of generalization for question answering on knowledge bases.

Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation Beyond iid: three levels of generalization for question answering on knowledge bases

Reference 16

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Observation 13b0a450-0aec-4a66-a123-899e8d30c235 · outbound

This paper cites Don't Generate, Discriminate: A Proposal for Grounding Language Models to Real-World Environments.

Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation Don't Generate, Discriminate: A Proposal for Grounding Language Models to Real-World Environments

Reference 17

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This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 18

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Observation 54f52d32-a48a-4d33-8486-3f73e6aaf418 · outbound

This paper cites Empowering GraphRAG with Knowledge Filtering and Integration.

Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation Empowering GraphRAG with Knowledge Filtering and Integration

Reference 19

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Observation 1d8b6382-e64c-4cef-80ed-67c82e4ff21c · outbound

This paper cites How Do LLMs Perform Two-Hop Reasoning in Context?.

Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation How Do LLMs Perform Two-Hop Reasoning in Context?

Reference 20

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Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation LightRAG: Simple and Fast Retrieval-Augmented Generation

Reference 21

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This paper cites J., Shu, Y., Gu, Y., Yasunaga, M., and Su, Y.

Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation J., Shu, Y., Gu, Y., Yasunaga, M., and Su, Y

Reference 22

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Observation bb81b3d3-10a4-4c9b-bb02-2c08c2e1cac3 · outbound

This paper cites From RAG to Memory: Non-Parametric Continual Learning for Large Language Models.

Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation From RAG to Memory: Non-Parametric Continual Learning for Large Language Models

Reference 23

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Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation Retrieval-Augmented Generation with Graphs (GraphRAG)

Reference 24

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This paper cites Gasket RAG : Systematic alignment of large language models with retrievers, 2025.

Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation Gasket RAG : Systematic alignment of large language models with retrievers, 2025

Reference 25

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This paper cites G-retriever: Retrieval-augmented generation for textual graph understanding and question answering.

Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation G-retriever: Retrieval-augmented generation for textual graph understanding and question answering

Reference 26

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This paper cites RULER: What's the Real Context Size of Your Long-Context Language Models?.

Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation RULER: What's the Real Context Size of Your Long-Context Language Models?

Reference 27

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Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W., et al

Reference 28

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This paper cites A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions.

Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions

Reference 29

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Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation Advancing Transformer Architecture in Long-Context Large Language Models: A Comprehensive Survey

Reference 30

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This paper cites J., Madotto, A., and Fung, P.

Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation J., Madotto, A., and Fung, P

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:31:39.955271Z

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Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation UniKGQA: Unified Retrieval and Reasoning for Solving Multi-hop Question Answering Over Knowledge Graph

Reference 32

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Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation StructGPT: A General Framework for Large Language Model to Reason over Structured Data

Reference 33

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Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation Long-Context LLMs Meet RAG: Overcoming Challenges for Long Inputs in RAG

Reference 34

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Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation A., Choi, Y., Inui, K., et al

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This paper cites Simple Is Effective: The Roles of Graphs and Large Language Models in Knowledge-Graph-Based Retrieval-Augmented Generation.

Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation Simple Is Effective: The Roles of Graphs and Large Language Models in Knowledge-Graph-Based Retrieval-Augmented Generation

Reference 36

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This paper cites Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text Analytics? A Study on Several Typical Tasks.

Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text Analytics? A Study on Several Typical Tasks

Reference 37

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Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation RAG-DDR: Optimizing Retrieval-Augmented Generation Using Differentiable Data Rewards

Reference 38

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Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation Towards General Text Embeddings with Multi-stage Contrastive Learning

Reference 39

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Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation Dual Reasoning: A GNN-LLM Collaborative Framework for Knowledge Graph Question Answering

Reference 40

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This paper cites A Survey on Hallucination in Large Vision-Language Models.

Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation A Survey on Hallucination in Large Vision-Language Models

Reference 41

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Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation A comprehensive survey on long context language modeling

Reference 42

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This paper cites F., Lin, K., Hewitt, J., Paranjape, A., Bevilacqua, M., Petroni, F., and Liang, P.

Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation F., Lin, K., Hewitt, J., Paranjape, A., Bevilacqua, M., Petroni, F., and Liang, P

Reference 43

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Observation fa96f278-c8b1-42c6-98e6-7ff636df3bce · outbound

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Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation KBQA-o1: Agentic Knowledge Base Question Answering with Monte Carlo Tree Search

Reference 44

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Observation 37d261e0-6b9b-4b00-8b0a-1034122a7596 · outbound

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Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning

Reference 45

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Observation 3f6d18c6-370f-4ff9-b8a1-4f3b6a330c89 · outbound

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Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation Gfm-rag: Graph foundation model for retrieval augmented generation

Reference 46

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Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning

Reference 47

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This paper cites MTEB: Massive Text Embedding Benchmark.

Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation MTEB: Massive Text Embedding Benchmark

Reference 48

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This paper cites Openai o3-mini.

Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation Openai o3-mini

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:31:39.445155Z

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Observation f88dd4aa-a89f-4ff1-8e60-6b0bb32df3a2 · outbound

This paper cites Graph Retrieval-Augmented Generation: A Survey.

Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation Graph Retrieval-Augmented Generation: A Survey

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Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation Unresolved cited work

Reference 51

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Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation O'Reilly Media, Inc

Reference 52

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Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation REPLUG: Retrieval-Augmented Black-Box Language Models

Reference 53

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Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models

Reference 54

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Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph

Reference 55

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Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation The Web as a Knowledge-base for Answering Complex Questions

Reference 56

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Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Reference 57

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Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation and Kr \"o tzsch, M

Reference 58

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Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation Knowledge-Driven CoT: Exploring Faithful Reasoning in LLMs for Knowledge-intensive Question Answering

Reference 59

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Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation Learning to Filter Context for Retrieval-Augmented Generation

Reference 60

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Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation How Easily do Irrelevant Inputs Skew the Responses of Large Language Models?

Reference 61

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Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation Efficient Streaming Language Models with Attention Sinks

Reference 62

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Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation Interactive-KBQA: Multi-Turn Interactions for Knowledge Base Question Answering with Large Language Models

Reference 63

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Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation Harnessing the power of llms in practice: A survey on chatgpt and beyond

Reference 64

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Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation APE: Faster and Longer Context-Augmented Generation via Adaptive Parallel Encoding

Reference 65

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Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation The value of semantic parse labeling for knowledge base question answering

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:31:38.680253Z

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Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation Making Retrieval-Augmented Language Models Robust to Irrelevant Context

Reference 67

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Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation Rankrag: Unifying context ranking with retrieval-augmented generation in llms

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:31:38.370834Z

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Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation Subgraph Retrieval Enhanced Model for Multi-hop Knowledge Base Question Answering

Reference 69

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Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation RAFT: Adapting Language Model to Domain Specific RAG

Reference 70

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Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation Length Extrapolation of Transformers: A Survey from the Perspective of Positional Encoding

Reference 71

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Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation write newline

Reference 72

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