NeuroGenesysThe research we build on.
1,514 papers by other researchers that our work cites, from continual learning and verification to language models, agents and hardware. Each entry links to the paper on arXiv and to where we use it. 301 are cited on published pages of this site; the rest in the research notes behind our studies. Our own papers are in the archive.
- 2025-04arXiv 2504.02107TiC-LM: A Web-Scale Benchmark for Time-Continual LLM Pretraining
Jeffrey Li, Mohammadreza Armandpour, Iman Mirzadeh et al. · Machine learning
Large Language Models (LLMs) trained on historical web data inevitably become outdated. We investigate evaluation strategies and update methods for LLMs as new data becomes available.
Cited inEWC protects what its Fisher sees: a literature synthesis on elastic weight consolidation, scale and coherenceStudy: Continual learning order and companion dataStudy: Elastic weight consolidation deep diveStudy: Fortifying knowledge in continual learningStudy: Hardware guardian core for continual learning - 2025-03arXiv 2503.20018Experience Replay Addresses Loss of Plasticity in Continual Learning
Jiuqi Wang, Rohan Chandra, Shangtong Zhang · Machine learning
Loss of plasticity is one of the main challenges in continual learning with deep neural networks, where neural networks trained via backpropagation gradually lose their ability to adapt to new tasks and perform significantly worse than their freshly initialized counterparts.
- 2025-03arXiv 2503.19206Overtrained Language Models Are Harder to Fine-Tune
Jacob Mitchell Springer, Sachin Goyal, Kaiyue Wen et al. · Language models & NLP
Large language models are pre-trained on ever-growing token budgets under the assumption that better pre-training performance translates to improved downstream models.
- 2025-03arXiv 2503.13575Analytic Subspace Routing: How Recursive Least Squares Works in Continual Learning of Large Language Model
Kai Tong, Kang Pan, Xiao Zhang et al. · Machine learning
Large Language Models (LLMs) possess encompassing capabilities that can process diverse language-related tasks. However, finetuning on LLMs will diminish this general skills and continual finetuning will further cause severe degradation on accumulated knowledge.
- 2025-03arXiv 2503.05683WikiBigEdit: Understanding the Limits of Lifelong Knowledge Editing in LLMs
Lukas Thede, Karsten Roth, Matthias Bethge et al. · Language models & NLP
Keeping large language models factually up-to-date is crucial for deployment, yet costly retraining remains a challenge. Knowledge editing offers a promising alternative, but methods are only tested on small-scale or synthetic edit benchmarks.
Cited inEWC protects what its Fisher sees: a literature synthesis on elastic weight consolidation, scale and coherenceStudy: Consequence consistent updatesStudy: Continual learning order and companion dataStudy: Elastic weight consolidation deep diveStudy: Fortifying knowledge in continual learningStudy: LLMs modifying open model weightsStudy: Logical coherence of LLM world modelsStudy: Orchestrated specialists and bridge synthesisStudy: Weight editing to prevent forgetting - 2025-02arXiv 2502.11177The Mirage of Model Editing: Revisiting Evaluation in the Wild
Wanli Yang, Fei Sun, Jiajun Tan et al. · Language models & NLP
Despite near-perfect results reported in the literature, the effectiveness of model editing in real-world applications remains unclear.
- 2025-02arXiv 2502.00592M+: Extending MemoryLLM with Scalable Long-Term Memory
Yu Wang, Dmitry Krotov, Yuanzhe Hu et al. · Language models & NLP
Equipping large language models (LLMs) with latent-space memory has attracted increasing attention as they can extend the context window of existing language models.
- 2025-01arXiv 2501.13453Spurious Forgetting in Continual Learning of Language Models
Junhao Zheng, Xidi Cai, Shengjie Qiu et al. · Machine learning
Recent advancements in large language models (LLMs) reveal a perplexing phenomenon in continual learning: despite extensive training, models experience significant performance declines, raising questions about task alignment and underlying knowledge retention.
- 2025-01arXiv 2501.13669How to Alleviate Catastrophic Forgetting in LLMs Finetuning? Hierarchical Layer-Wise and Element-Wise Regularization
Shezheng Song, Hao Xu, Jun Ma et al. · Language models & NLP
Large Language Models (LLMs) exhibit strong general language capabilities. However, fine-tuning these models on domain-specific tasks often leads to catastrophic forgetting, where the model overwrites or loses essential knowledge acquired during pretraining.
- 2024-12arXiv 2501.00663Titans: Learning to Memorize at Test Time
Ali Behrouz, Peilin Zhong, Vahab Mirrokni · Machine learning
Over more than a decade there has been an extensive research effort on how to effectively utilize recurrent models and attention.
- 2024-12arXiv 2412.19512Safeguard Fine-Tuned LLMs Through Pre- and Post-Tuning Model Merging
Hua Farn, Hsuan Su, Shachi H Kumar et al. · Language models & NLP
Fine-tuning large language models (LLMs) for downstream tasks often leads to catastrophic forgetting, notably degrading the safety of originally aligned models.
- 2024-10arXiv 2410.21228LoRA vs Full Fine-tuning: An Illusion of Equivalence
Reece Shuttleworth, Jacob Andreas, Antonio Torralba et al. · Machine learning
Fine-tuning is a crucial paradigm for adapting pre-trained large language models to downstream tasks. Recently, methods like Low-Rank Adaptation (LoRA) have been shown to effectively fine-tune LLMs with an extreme reduction in trainable parameters.
- 2024-10arXiv 2410.20098Self-Normalized Resets for Plasticity in Continual Learning
Vivek F. Farias, Adam D. Jozefiak · Machine learning
Plasticity Loss is an increasingly important phenomenon that refers to the empirical observation that as a neural network is continually trained on a sequence of changing tasks, its ability to adapt to a new task diminishes over time.
- 2024-10arXiv 2410.09870ChroKnowledge: Unveiling Chronological Knowledge of Language Models in Multiple Domains
Yein Park, Chanwoong Yoon, Jungwoo Park et al. · Language models & NLP
Large language models (LLMs) have brought significant changes to many aspects of our lives. However, assessing and ensuring their chronological knowledge remains challenging.
- 2024-10arXiv 2410.08827Do Unlearning Methods Remove Information from Language Model Weights?
Aghyad Deeb, Fabien Roger · Machine learning
Large Language Models' knowledge of how to perform cyber-security attacks, create bioweapons, and manipulate humans poses risks of misuse. Previous work has proposed methods to unlearn this knowledge.
- 2024-10arXiv 2410.07163Simplicity Prevails: Rethinking Negative Preference Optimization for LLM Unlearning
Chongyu Fan, Jiancheng Liu, Licong Lin et al. · Language models & NLP
, copyrighted or harmful content) while preserving model utility. Despite the increasing demand for unlearning, a technically-grounded optimization framework is lacking.
- 2024-10arXiv 2410.03617What Matters for Model Merging at Scale?
Prateek Yadav, Tu Vu, Jonathan Lai et al. · Machine learning
Model merging aims to combine multiple expert models into a more capable single model, offering benefits such as reduced storage and serving costs, improved generalization, and support for decentralized model development.
- 2024-10arXiv 2410.02355AlphaEdit: Null-Space Constrained Knowledge Editing for Language Models
Junfeng Fang, Houcheng Jiang, Kun Wang et al. · 13th International Conference on Learning Representations (ICLR 2025 Oral) · Language models & NLP
Large language models (LLMs) often exhibit hallucinations due to incorrect or outdated knowledge. Hence, model editing methods have emerged to enable targeted knowledge updates.
- 2024-10arXiv 2410.02879Position: LLM Unlearning Benchmarks are Weak Measures of Progress
Pratiksha Thaker, Shengyuan Hu, Neil Kale et al. · Language models & NLP
Unlearning methods have the potential to improve the privacy and safety of large language models (LLMs) by removing sensitive or harmful information post hoc. The LLM unlearning research community has increasingly turned toward empirical benchmarks to assess the effectiveness of such methods.
- 2024-10arXiv 2410.03782DaWin: Training-free Dynamic Weight Interpolation for Robust Adaptation
Changdae Oh, Yixuan Li, Kyungwoo Song et al. · Machine learning
Adapting a pre-trained foundation model on downstream tasks should ensure robustness against distribution shifts without the need to retrain the whole model.
- 2024-10arXiv 2410.01380Knowledge Entropy Decay during Language Model Pretraining Hinders New Knowledge Acquisition
Jiyeon Kim, Hyunji Lee, Hyowon Cho et al. · Language models & NLP
In this work, we investigate how a model's tendency to broadly integrate its parametric knowledge evolves throughout pretraining, and how this behavior affects overall performance, particularly in terms of knowledge acquisition and forgetting.
- 2024-09arXiv 2409.18025An Adversarial Perspective on Machine Unlearning for AI Safety
Jakub Łucki, Boyi Wei, Yangsibo Huang et al. · Machine learning
Large language models are finetuned to refuse questions about hazardous knowledge, but these protections can often be bypassed. Unlearning methods aim at completely removing hazardous capabilities from models and make them inaccessible to adversaries.
- 2024-08arXiv 2408.07666Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities
Enneng Yang, Li Shen, Guibing Guo et al. · Machine learning
Model merging is an efficient empowerment technique in the machine learning community that does not require the collection of raw training data and does not require expensive computation.
- 2024-07arXiv 2407.06322MagMax: Leveraging Model Merging for Seamless Continual Learning
Daniel Marczak, Bartłomiej Twardowski, Tomasz Trzciński et al. · Machine learning
This paper introduces a continual learning approach named MagMax, which utilizes model merging to enable large pre-trained models to continuously learn from new data without forgetting previously acquired knowledge.
- 2024-06arXiv 2406.13356Unlearning or Obfuscating? Jogging the Memory of Unlearned LLMs via Benign Relearning
Shengyuan Hu, Yiwei Fu, Zhiwei Steven Wu et al. · Machine learning
Machine unlearning is a promising approach to mitigate undesirable memorization of training data in ML models. However, in this work we show that existing approaches for unlearning in LLMs are surprisingly susceptible to a simple set of .
- 2024-05arXiv 2405.14768WISE: Rethinking the Knowledge Memory for Lifelong Model Editing of Large Language Models
Peng Wang, Zexi Li, Ningyu Zhang et al. · Language models & NLP
Large language models (LLMs) need knowledge updates to meet the ever-growing world facts and correct the hallucinated responses, facilitating the methods of lifelong model editing.
Cited inEWC protects what its Fisher sees: a literature synthesis on elastic weight consolidation, scale and coherenceStudy: Consequence consistent updatesStudy: Elastic weight consolidation deep diveStudy: Fortifying knowledge in continual learningStudy: LLMs modifying open model weightsStudy: Orchestrated specialists and bridge synthesis - 2024-05arXiv 2405.09673LoRA Learns Less and Forgets Less
Dan Biderman, Jacob Portes, Jose Javier Gonzalez Ortiz et al. · Machine learning
Low-Rank Adaptation (LoRA) is a widely-used parameter-efficient finetuning method for large language models. LoRA saves memory by training only low rank perturbations to selected weight matrices.
- 2024-05arXiv 2405.05904Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?
Zorik Gekhman, Gal Yona, Roee Aharoni et al. · Language models & NLP
When large language models are aligned via supervised fine-tuning, they may encounter new factual information that was not acquired through pre-training.
Cited inStable wrong beliefs evade every self-check: a literature synthesis on model self-knowledge and confident errorsTo stop a model forgetting, rehearse what it knew. New context won't do it.Study: Consequence consistent updatesStudy: Continual learning order and companion dataStudy: Fortifying knowledge in continual learningStudy: Generational competitive continual learningStudy: Graded succession by earned authorityStudy: Logical coherence of LLM world modelsStudy: Model self knowledge and confident errorsStudy: Orchestrated specialists and bridge synthesisStudy: Stopping confident wrong expert votes - 2024-04arXiv 2404.10198ClashEval: Quantifying the tug-of-war between an LLM's internal prior and external evidence
Kevin Wu, Eric Wu, James Zou · Language models & NLP
Retrieval augmented generation (RAG) is frequently used to mitigate hallucinations and provide up-to-date knowledge for large language models (LLMs).
Cited inStable wrong beliefs evade every self-check: a literature synthesis on model self-knowledge and confident errorsTo keep what a model knows, control what it is allowed to moveWhat a model lets into long-term memory matters more than how it learnsStudy: Fortifying knowledge in continual learningStudy: Hardware guardian core for continual learningStudy: Memory world models and source trustStudy: Model self knowledge and confident errors - 2024-04arXiv 2404.05868Negative Preference Optimization: From Catastrophic Collapse to Effective Unlearning
Ruiqi Zhang, Licong Lin, Yu Bai et al. · Machine learning
Large Language Models (LLMs) often memorize sensitive, private, or copyrighted data during pre-training. LLM unlearning aims to eliminate the influence of undesirable data from the pre-trained model while preserving the model's utilities on other tasks.
- 2024-03arXiv 2403.08319Knowledge Conflicts for LLMs: A Survey
Rongwu Xu, Zehan Qi, Zhijiang Guo et al. · Language models & NLP
This survey provides an in-depth analysis of knowledge conflicts for large language models (LLMs), highlighting the complex challenges they encounter when blending contextual and parametric knowledge.
- 2024-03arXiv 2403.08763Simple and Scalable Strategies to Continually Pre-train Large Language Models
Adam Ibrahim, Benjamin Thérien, Kshitij Gupta et al. · Machine learning
Large language models (LLMs) are routinely pre-trained on billions of tokens, only to start the process over again once new data becomes available. A much more efficient solution is to continually pre-train these models, saving significant compute compared to re-training.
Cited inTo keep what a model knows, control what it is allowed to moveStudy: Elastic weight consolidation deep diveStudy: Fortifying knowledge in continual learningStudy: Hardware guardian core for continual learningStudy: Hierarchical and T shaped MoEStudy: Orchestrated specialists and bridge synthesis - 2024-03arXiv 2403.07816Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM
Sainbayar Sukhbaatar, Olga Golovneva, Vasu Sharma et al. · Language models & NLP
We investigate efficient methods for training Large Language Models (LLMs) to possess capabilities in multiple specialized domains, such as coding, math reasoning and world knowledge.
Cited inRoute on shared statistics, never expert confidence: the router architecture for frozen expertsStudy: Fortifying knowledge in continual learningStudy: Hierarchical and T shaped MoEStudy: Merging and splitting MoE expertsStudy: Optimal router architectureStudy: Orchestrated specialists and bridge synthesis - 2024-03arXiv 2403.03218The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning
Nathaniel Li, Alexander Pan, Anjali Gopal et al. · Machine learning
The White House Executive Order on Artificial Intelligence highlights the risks of large language models (LLMs) empowering malicious actors in developing biological, cyber, and chemical weapons.
- 2024-02arXiv 2402.18762Disentangling the Causes of Plasticity Loss in Neural Networks
Clare Lyle, Zeyu Zheng, Khimya Khetarpal et al. · Machine learning
Underpinning the past decades of work on the design, initialization, and optimization of neural networks is a seemingly innocuous assumption: that the network is trained on a \textit{stationary} data distribution.
- 2024-02arXiv 2402.01364Continual Learning for Large Language Models: A Survey
Tongtong Wu, Linhao Luo, Yuan-Fang Li et al. · Language models & NLP
Large language models (LLMs) are not amenable to frequent re-training, due to high training costs arising from their massive scale. However, updates are necessary to endow LLMs with new skills and keep them up-to-date with rapidly evolving human knowledge.
- 2024-01arXiv 2401.12617The Joint Effect of Task Similarity and Overparameterization on Catastrophic Forgetting -- An Analytical Model
Daniel Goldfarb, Itay Evron, Nir Weinberger et al. · Machine learning
In continual learning, catastrophic forgetting is affected by multiple aspects of the tasks. Previous works have analyzed separately how forgetting is affected by either task similarity or overparameterization.
- 2024-01arXiv 2401.07453Model Editing at Scale leads to Gradual and Catastrophic Forgetting
Akshat Gupta, Anurag Rao, Gopala Anumanchipalli · Language models & NLP
Editing knowledge in large language models is an attractive capability to have which allows us to correct incorrectly learnt facts during pre-training, as well as update the model with an ever-growing list of new facts.
- 2024-01arXiv 2401.05605Scaling Laws for Forgetting When Fine-Tuning Large Language Models
Damjan Kalajdzievski · Language models & NLP
We study and quantify the problem of forgetting when fine-tuning pre-trained large language models (LLMs) on a downstream task. We find that parameter-efficient fine-tuning (PEFT) strategies, such as Low-Rank Adapters (LoRA), still suffer from catastrophic forgetting.
- 2024-01arXiv 2401.06121TOFU: A Task of Fictitious Unlearning for LLMs
Pratyush Maini, Zhili Feng, Avi Schwarzschild et al. · Machine learning
Large language models trained on massive corpora of data from the web can memorize and reproduce sensitive or private data raising both legal and ethical concerns.
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