Which Neurons Matter in IR? Applying Integrated Gradients-based Methods to Understand Cross-Encoders

• URL: http://arxiv.org/abs/2406.19309v2
• Date: Fri, 05 Jul 2024 15:48:55 GMT
• Title: Which Neurons Matter in IR? Applying Integrated Gradients-based Methods to Understand Cross-Encoders
• Abstract summary: In this paper, we explore the possibility of adapting Integrated Gradient-based methods in an Information Retrieval context to identify the role of individual neurons. We provide new insights into the role of what we call "relevance" neurons, as well as how they deal with unseen data.
• Score: 11.192264101562786
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the recent addition of Retrieval-Augmented Generation (RAG), the scope and importance of Information Retrieval (IR) has expanded. As a result, the importance of a deeper understanding of IR models also increases. However, interpretability in IR remains under-explored, especially when it comes to the models' inner mechanisms. In this paper, we explore the possibility of adapting Integrated Gradient-based methods in an IR context to identify the role of individual neurons within the model. In particular, we provide new insights into the role of what we call "relevance" neurons, as well as how they deal with unseen data. Finally, we carry out an in-depth pruning study to validate our findings.

Related papers

• Brain-Machine Interfaces & Information Retrieval Challenges and Opportunities [6.084958172018792]
  This perspective paper offers a comprehensive analysis of how Brain-Machine Interface (BMI) technology could transform Information Retrieval (IR) systems. We identify specific research opportunities and propose concrete directions for developing BMI-enhanced IR systems. We conclude by examining critical technical and ethical challenges in implementing these advances.
  arXiv  Detail & Related papers  (2025-04-14T16:18:30Z)
• MechIR: A Mechanistic Interpretability Framework for Information Retrieval [31.07664679437899]
  Mechanistic interpretability is an emerging diagnostic approach for neural models. This work comprises a flexible framework for diagnostic analysis and intervention within highly parametric neural systems.
  arXiv  Detail & Related papers  (2025-01-17T12:56:28Z)
• Machine Learning on Dynamic Functional Connectivity: Promise, Pitfalls, and Interpretations [7.013079422694949]
  We seek to establish a well-founded empirical guideline for designing deep models for functional neuroimages. We put the spotlight on (1) What is the current state-of-the-arts (SOTA) performance in cognitive task recognition and disease diagnosis using fMRI? We have conducted a comprehensive evaluation and statistical analysis, in various settings, to answer the above outstanding questions.
  arXiv  Detail & Related papers  (2024-09-17T17:24:17Z)
• Robust Neural Information Retrieval: An Adversarial and Out-of-distribution Perspective [111.58315434849047]
  robustness of neural information retrieval models (IR) models has garnered significant attention. We view the robustness of IR to be a multifaceted concept, emphasizing its necessity against adversarial attacks, out-of-distribution (OOD) scenarios and performance variance. We provide an in-depth discussion of existing methods, datasets, and evaluation metrics, shedding light on challenges and future directions in the era of large language models.
  arXiv  Detail & Related papers  (2024-07-09T16:07:01Z)
• A Systematic Review of Deep Learning-based Research on Radiology Report Generation [19.36297532792359]
  Radiology report generation (RRG) aims to automatically generate free-text descriptions from clinical radiographs. Research on RRG is experiencing explosive growth in the past half-decade, especially with the rapid development of deep learning approaches.
  arXiv  Detail & Related papers  (2023-11-23T20:52:44Z)
• Large Language Models for Information Retrieval: A Survey [58.30439850203101]
  Information retrieval has evolved from term-based methods to its integration with advanced neural models. Recent research has sought to leverage large language models (LLMs) to improve IR systems. We delve into the confluence of LLMs and IR systems, including crucial aspects such as query rewriters, retrievers, rerankers, and readers.
  arXiv  Detail & Related papers  (2023-08-14T12:47:22Z)
• NeuroExplainer: Fine-Grained Attention Decoding to Uncover Cortical Development Patterns of Preterm Infants [73.85768093666582]
  We propose an explainable geometric deep network dubbed NeuroExplainer. NeuroExplainer is used to uncover altered infant cortical development patterns associated with preterm birth.
  arXiv  Detail & Related papers  (2023-01-01T12:48:12Z)
• Entity-Conditioned Question Generation for Robust Attention Distribution in Neural Information Retrieval [51.53892300802014]
  We show that supervised neural information retrieval models are prone to learning sparse attention patterns over passage tokens. Using a novel targeted synthetic data generation method, we teach neural IR to attend more uniformly and robustly to all entities in a given passage.
  arXiv  Detail & Related papers  (2022-04-24T22:36:48Z)
• Continual Learning of Long Topic Sequences in Neural Information Retrieval [2.3846478553599098]
  We first propose a dataset based upon the MSMarco corpus aiming at modeling a long stream of topics. We then in-depth analyze the ability of recent neural IR models while continually learning those streams.
  arXiv  Detail & Related papers  (2022-01-10T14:19:09Z)
• Learning Neural Causal Models with Active Interventions [83.44636110899742]
  We introduce an active intervention-targeting mechanism which enables a quick identification of the underlying causal structure of the data-generating process. Our method significantly reduces the required number of interactions compared with random intervention targeting. We demonstrate superior performance on multiple benchmarks from simulated to real-world data.
  arXiv  Detail & Related papers  (2021-09-06T13:10:37Z)
• ACRE: Abstract Causal REasoning Beyond Covariation [90.99059920286484]
  We introduce the Abstract Causal REasoning dataset for systematic evaluation of current vision systems in causal induction. Motivated by the stream of research on causal discovery in Blicket experiments, we query a visual reasoning system with the following four types of questions in either an independent scenario or an interventional scenario. We notice that pure neural models tend towards an associative strategy under their chance-level performance, whereas neuro-symbolic combinations struggle in backward-blocking reasoning.
  arXiv  Detail & Related papers  (2021-03-26T02:42:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.