# Relevance Feedback with Brain Signals

arXiv: https://arxiv.org/abs/2312.05669 · HTML: https://mlsanity.com/paper/2312.05669/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 90 references that are in the map.
- First posted on arXiv 9 Dec 2023.

## Builds on (most cited first, 17 of 17)

- [Pretrained Transformers for Text Ranking: BERT and Beyond](https://mlsanity.com/paper/2010.06467/) (2020)
- [Unbiased Learning-to-Rank with Biased Feedback](https://mlsanity.com/paper/1608.04468/) (2016)
- [End-to-End Open-Domain Question Answering with BERTserini](https://mlsanity.com/paper/1902.01718/) (2019)
- [EEG-Based Emotion Recognition Using Regularized Graph Neural Networks](https://mlsanity.com/paper/1907.07835/) (2019)
- [Rethink Training of BERT Rerankers in Multi-Stage Retrieval Pipeline](https://mlsanity.com/paper/2101.08751/) (2021)
- [T2Ranking: A large-scale Chinese Benchmark for Passage Ranking](https://mlsanity.com/paper/2304.03679/) (2023)
- [Improving Query Representations for Dense Retrieval with Pseudo Relevance Feedback](https://mlsanity.com/paper/2108.13454/) (2021)
- [Query expansion techniques for information retrieval: A survey](https://mlsanity.com/paper/1708.00247/) (2017)
- [BERT-QE: Contextualized Query Expansion for Document Re-ranking](https://mlsanity.com/paper/2009.07258/) (2020)
- [Pseudo-Relevance Feedback for Multiple Representation Dense Retrieval](https://mlsanity.com/paper/2106.11251/) (2021)
- [Conversational Product Search Based on Negative Feedback](https://mlsanity.com/paper/1909.02071/) (2019)
- [Natural brain-information interfaces: Recommending information by relevance inferred from human brain signals](https://mlsanity.com/paper/1607.03502/) (2016)
- [Web Search via an Efficient and Effective Brain-Machine Interface](https://mlsanity.com/paper/2110.07225/) (2021)
- [How does Feedback Signal Quality Impact Effectiveness of Pseudo Relevance Feedback for Passage Retrieval?](https://mlsanity.com/paper/2205.05888/) (2022)
- [Iterative Relevance Feedback for Answer Passage Retrieval with Passage-level Semantic Match](https://mlsanity.com/paper/1812.08870/) (2018)
- [Brain Topography Adaptive Network for Satisfaction Modeling in Interactive Information Access System](https://mlsanity.com/paper/2208.08097/) (2022)
- [Revisiting Iterative Relevance Feedback for Document and Passage Retrieval](https://mlsanity.com/paper/1812.05731/) (2018)

## Built on by (most cited first, 4 of 4)

- [A Comprehensive Survey on Multi-modal Conversational Emotion Recognition with Deep Learning](https://mlsanity.com/paper/2312.05735/) (2023)
- [Mitigating Entity-Level Hallucination in Large Language Models](https://mlsanity.com/paper/2407.09417/) (2024)
- [CrossPT-EEG: A Benchmark for Cross-Participant and Cross-Time Generalization of EEG-based Visual Decoding](https://mlsanity.com/paper/2406.07151/) (2024)
- [How do Humans Process AI-generated Hallucination Contents: a Neuroimaging Study](https://mlsanity.com/paper/2605.16953/) (2026)

## Abstract (arXiv)

The Relevance Feedback (RF) process relies on accurate and real-time relevance estimation of feedback documents to improve retrieval performance. Since collecting explicit relevance annotations imposes an extra burden on the user, extensive studies have explored using pseudo-relevance signals and implicit feedback signals as substitutes. However, such signals are indirect indicators of relevance and suffer from complex search scenarios where user interactions are absent or biased. Recently, the advances in portable and high-precision brain-computer interface (BCI) devices have shown the possibility to monitor user's brain activities during search process. Brain signals can directly reflect user's psychological responses to search results and thus it can act as additional and unbiased RF signals. To explore the effectiveness of brain signals in the context of RF, we propose a novel RF framework that combines BCI-based relevance feedback with pseudo-relevance signals and implicit signals to improve the performance of document re-ranking. The experimental results on the user study dataset show that incorporating brain signals leads to significant performance improvement in our RF framework. Besides, we observe that brain signals perform particularly well in several hard search scenarios, especially when implicit signals as feedback are missing or noisy. This reveals when and how to exploit brain signals in the context of RF.