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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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A multiple relevance feedback strategy with positive and negative models.

Yunlong Ma1, Hongfei Lin1

  • 1Information Retrieval Laboratory, School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning, China.

Plos One
|August 20, 2014
PubMed
Summary
This summary is machine-generated.

For difficult search queries, using both positive and negative document examples improves retrieval accuracy. A novel language model-based feedback method combining both strategies outperforms existing techniques.

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Area of Science:

  • Information Retrieval
  • Computer Science

Background:

  • Relevance feedback techniques commonly use positive examples to enhance search accuracy.
  • Positive feedback is insufficient for difficult queries yielding poor initial results.
  • Negative feedback is effective when relevant documents are scarce in initial retrieval sets.

Purpose of the Study:

  • To investigate multiple relevance feedback strategies for difficult search queries.
  • To improve information retrieval accuracy when initial results are highly unsatisfactory.

Main Methods:

  • A novel language model-based multiple model feedback approach was developed.
  • The method utilizes both positive and negative examples from the first-pass retrieval.
  • Experiments were conducted on TREC collections.

Main Results:

  • The proposed multiple model feedback method demonstrated superior effectiveness.
  • It outperformed baseline methods and strategies using only positive or negative feedback.
  • Effectiveness was shown for scenarios with at most three relevant documents in the top twenty.

Conclusions:

  • Combining positive and negative feedback is crucial for improving retrieval on difficult queries.
  • Language model-based multiple model feedback offers a robust solution for challenging information retrieval tasks.