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Who works on “RAG for Reducing LLM Hallucinations”? The teams, people and approaches

From a topic, the tool read the papers on it and grouped who signs them: institutions, people, the approaches they take and the countries where the work is done.

A real example: what you get

15 institutions, 30 people and 118 papers on “RAG for Reducing LLM Hallucinations”.

  • The landscape of the topic
  • 4 approaches, each named and described
  • 30 people who sign first, last or as corresponding author
  • 13 countries
  • Exports, a collection of the papers, a monthly follow-up
The landscape

The retrieval-quality approach focuses on improving evidence selection and grounding within RAG, whereas the architecture approach examines the broader design, efficiency, and knowledge-integration choices of RAG systems. The combined-validation approach treats retrieval as one component of a wider hallucination-detection and mitigation toolkit, overlapping with RAG-specific work on confidence and verification. The task-specific approach brings these concerns to particular outputs, domains, …

An approach

These papers review and develop RAG-specific hallucination mitigation through retrieval comparisons, retrieval optimization, confidence-aware retrieval, self-consistency verification, and handling evidence from multiple sources.

Retrieval Quality and Grounding · 62 papers

The first results

  1. Tsinghua University CN · 3 papers
  2. Amazon (United States) US · 4 papers
  3. Fudan University CN · 2 papers
  4. Tencent (China) CN · 3 papers
  5. University of the Cumberlands US · 2 papers
  6. Renmin University of China CN · 2 papers
  7. Peking University CN · 2 papers
  8. Huawei Technologies (China) CN · 2 papers
  9. Amnesty International Ireland IE · 1 paper
  10. Amrita Vishwa Vidyapeetham IN · 1 paper

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