# Relevance feedback strategies for recall-oriented neural information retrieval

arXiv: https://arxiv.org/abs/2311.15110 · HTML: https://mlsanity.com/paper/2311.15110/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 29 references that are in the map.
- First posted on arXiv 25 Nov 2023.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Evolution of Semantic Similarity -- A Survey](https://mlsanity.com/paper/2004.13820/) (2020)
- [Pseudo Relevance Feedback with Deep Language Models and Dense Retrievers: Successes and Pitfalls](https://mlsanity.com/paper/2108.11044/) (2021)
- [Evaluating Sentence-Level Relevance Feedback for High-Recall Information Retrieval](https://mlsanity.com/paper/1803.08988/) (2018)
- [Probably Reasonable Search in eDiscovery](https://mlsanity.com/paper/2201.12376/) (2022)

## Built on by (most cited first, 1 of 1)

- [The emergence of Large Language Models (LLM) as a tool in literature reviews: an LLM automated systematic review](https://mlsanity.com/paper/2409.04600/) (2024)

## Abstract (arXiv)

In a number of information retrieval applications (e.g., patent search, literature review, due diligence, etc.), preventing false negatives is more important than preventing false positives. However, approaches designed to reduce review effort (like "technology assisted review") can create false negatives, since they are often based on active learning systems that exclude documents automatically based on user feedback. Therefore, this research proposes a more recall-oriented approach to reducing review effort. More specifically, through iteratively re-ranking the relevance rankings based on user feedback, which is also referred to as relevance feedback. In our proposed method, the relevance rankings are produced by a BERT-based dense-vector search and the relevance feedback is based on cumulatively summing the queried and selected embeddings. Our results show that this method can reduce review effort between 17.85% and 59.04%, compared to a baseline approach (of no feedback), given a fixed recall target