Recall Initiator

Recall, in the context of machine learning and artificial intelligence, refers to a system's ability to retrieve relevant information from its memory, whether that memory is explicitly stored or implicitly encoded within model parameters. Current research focuses on improving recall in various applications, including trajectory prediction, multimodal embedding retrieval, and large language model (LLM) performance, often employing techniques like contrastive decoding, memory arrays, and hybrid loss functions to enhance accuracy and efficiency. These advancements are crucial for improving the performance and reliability of AI systems across diverse fields, from autonomous driving to e-commerce and medical device safety, by enabling more effective use of past experiences and knowledge.

Papers

October 3, 2024

Remember and Recall: Associative-Memory-based Trajectory Prediction
Hang Guo, Yuzhen Zhang, Tianci Gao, Junning Su, Pei Lv, Mingliang Xu
Trajectory Prediction Handwritten Trajectory Recall Initiator Memory Deep Learning

September 9, 2024

Recall: Empowering Multimodal Embedding for Edge Devices
Dongqi Cai, Shangguang Wang, Chen Peng, Zeling Zhang, Mengwei Xu
Multimodal Phenomenon Edge Device Retrieval Performance Recall Initiator Human Memory

September 5, 2024

Con-ReCall: Detecting Pre-training Data in LLMs via Contrastive Decoding
Cheng Wang, Yiwei Wang, Bryan Hooi, Yujun Cai, Nanyun Peng, Kai-Wei Chang
Large Language Model Medical LLM Raw Data Distribution Shift Membership Inference Contrastive Decoding Recall Initiator

August 27, 2024

June 25, 2024

Forget but Recall: Incremental Latent Rectification in Continual Learning
Nghia D. Nguyen, Hieu Trung Nguyen, Ang Li, Hoang Pham, Viet Anh Nguyen, Khoa D. Doan
Continual LEArning Incremental Learning Anti Forgetting Continual Learning Benchmark Tiny ImageNet Recall Initiator Latent Point Diffusion Model

June 23, 2024

ReCaLL: Membership Inference via Relative Conditional Log-Likelihoods
Roy Xie, Junlin Wang, Ruomin Huang, Minxing Zhang, Rong Ge, Jian Pei, Neil Zhenqiang Gong, Bhuwan Dhingra
Large Language Model Membership Inference Attack Membership Inference Recall Initiator Conditional Language Model Conditional Likelihood

June 14, 2024

In-depth analysis of recall initiators of medical devices with a Machine Learning-Natural language Processing workflow
Yang Hu
New Machine Depth Analysis Recall Initiator Medical Device

April 27, 2024

Recall, Retrieve and Reason: Towards Better In-Context Relation Extraction
Guozheng Li, Peng Wang, Wenjun Ke, Yikai Guo, Ke Ji, Ziyu Shang, Jiajun Liu, Zijie Xu
Context Learning App to App Retrieval Relation Extraction Reason Giving Entity Pair Recall Initiator

April 13, 2024

LLM In-Context Recall is Prompt Dependent
Daniel Machlab, Rick Battle
Recall Initiator LLM in Context Needle in a Haystack

February 16, 2024

Exploring Precision and Recall to assess the quality and diversity of LLMs
Florian Le Bronnec, Alexandre Verine, Benjamin Negrevergne, Yann Chevaleyre, Alexandre Allauzen
Large Language Model Text Generation Diversity Awareness Quality Issue Multidimensional Local Precision Rate Recall Initiator Open Ended Generation

January 30, 2024

Exploring Prime Number Classification: Achieving High Recall Rate and Rapid Convergence with Sparse Encoding
Serin Lee, S. Kim
Machine Learning Recall Initiator Convergence Performance Number Theory Prime Number

December 13, 2023

Learn or Recall? Revisiting Incremental Learning with Pre-trained Language Models
Junhao Zheng, Shengjie Qiu, Qianli Ma
Natural Language Processing Pre Trained Language Model Incremental Learning Recall Initiator Incremental Task

December 8, 2023

Zoology: Measuring and Improving Recall in Efficient Language Models
Simran Arora, Sabri Eyuboglu, Aman Timalsina, Isys Johnson, Michael Poli, James Zou, Atri Rudra, Christopher Ré
Language Model State of the Art Recall Initiator Gated Convolutional Neural Network

December 4, 2023

Explore, Select, Derive, and Recall: Augmenting LLM with Human-like Memory for Mobile Task Automation
Sunjae Lee, Junyoung Choi, Jungjae Lee, Munim Hasan Wasi, Hojun Choi, Steven Y. Ko, Sangeun Oh, Insik Shin
Large Language Model Medical LLM Recall Initiator Human Memory Human Like Memory Task Automation

November 14, 2023

RECALL: A Benchmark for LLMs Robustness against External Counterfactual Knowledge
Yi Liu, Lianzhe Huang, Shicheng Li, Sishuo Chen, Hao Zhou, Fandong Meng, Jie Zhou, Xu Sun
Knowledge Graph New Benchmark Question Answering External Knowledge AI Chatbots Recall Initiator Unreliable Source LLM Robustness

October 26, 2023

Neuro-Inspired Fragmentation and Recall to Overcome Catastrophic Forgetting in Curiosity
Jaedong Hwang, Zhang-Wei Hong, Eric Chen, Akhilan Boopathy, Pulkit Agrawal, Ila Fiete
Catastrophic Forgetting Reward Function Intrinsic Reward Recall Initiator Curiosity Inducing Situation Inspired Fragmentation

September 25, 2023

Evaluating Classification Systems Against Soft Labels with Fuzzy Precision and Recall
Manu Harju, Annamaria Mesaros
Multidimensional Local Precision Rate Cross Entropy Soft Label Recall Initiator Classification System Binary Label

September 19, 2023

RECALL+: Adversarial Web-based Replay for Continual Learning in Semantic Segmentation
Chang Liu, Giulia Rizzoli, Francesco Barbato, Andrea Maracani, Marco Toldo, Umberto Michieli, Yi Niu, Pietro Zanuttigh
Semantic Segmentation Continual LEArning Catastrophic Forgetting Pseudo Labeling Recall Initiator Web Crawled Data

September 4, 2023

Probabilistic Precision and Recall Towards Reliable Evaluation of Generative Models
Dogyun Park, Suhyun Kim
Generative Model Recall Initiator State of the Art Generative New Metric

Recall Initiator

Papers

Remember and Recall: Associative-Memory-based Trajectory Prediction

Recall: Empowering Multimodal Embedding for Edge Devices

Con-ReCall: Detecting Pre-training Data in LLMs via Contrastive Decoding

Relation Also Knows: Rethinking the Recall and Editing of Factual Associations in Auto-Regressive Transformer Language Models

MRSE: An Efficient Multi-modality Retrieval System for Large Scale E-commerce

Forget but Recall: Incremental Latent Rectification in Continual Learning

ReCaLL: Membership Inference via Relative Conditional Log-Likelihoods

In-depth analysis of recall initiators of medical devices with a Machine Learning-Natural language Processing workflow

Recall, Retrieve and Reason: Towards Better In-Context Relation Extraction

LLM In-Context Recall is Prompt Dependent

Exploring Precision and Recall to assess the quality and diversity of LLMs

Exploring Prime Number Classification: Achieving High Recall Rate and Rapid Convergence with Sparse Encoding

Learn or Recall? Revisiting Incremental Learning with Pre-trained Language Models

Zoology: Measuring and Improving Recall in Efficient Language Models

Explore, Select, Derive, and Recall: Augmenting LLM with Human-like Memory for Mobile Task Automation

RECALL: A Benchmark for LLMs Robustness against External Counterfactual Knowledge

Neuro-Inspired Fragmentation and Recall to Overcome Catastrophic Forgetting in Curiosity

Evaluating Classification Systems Against Soft Labels with Fuzzy Precision and Recall

RECALL+: Adversarial Web-based Replay for Continual Learning in Semantic Segmentation

Probabilistic Precision and Recall Towards Reliable Evaluation of Generative Models