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.
- 2024-03arXiv 2403.01244Mitigating Catastrophic Forgetting in Large Language Models with Self-Synthesized Rehearsal
Jianheng Huang, Leyang Cui, Ante Wang et al. · Language models & NLP
Large language models (LLMs) suffer from catastrophic forgetting during continual learning. Conventional rehearsal-based methods rely on previous training data to retain the model's ability, which may not be feasible in real-world applications.
- 2024-03arXiv 2403.01315Near-optimal Per-Action Regret Bounds for Sleeping Bandits
Quan Nguyen, Nishant A. Mehta · Machine learning
We derive near-optimal per-action regret bounds for sleeping bandits, in which both the sets of available arms and their losses in every round are chosen by an adversary.
- 2024-03arXiv 2403.00745AtP*: An efficient and scalable method for localizing LLM behaviour to components
János Kramár, Tom Lieberum, Rohin Shah et al. · Machine learning
Activation Patching is a method of directly computing causal attributions of behavior to model components. However, applying it exhaustively requires a sweep with cost scaling linearly in the number of model components, which can be prohibitively expensive for SoTA Large Language Models (LLMs).
- 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 2402.19379Wisdom of the Silicon Crowd: LLM Ensemble Prediction Capabilities Rival Human Crowd Accuracy
Philipp Schoenegger, Indre Tuminauskaite, Peter S. Park et al. · Society, software & economics
Human forecasting accuracy in practice relies on the 'wisdom of the crowd' effect, in which predictions about future events are significantly improved by aggregating across a crowd of individual forecasters.
- 2024-02arXiv 2402.18272Rethinking the Bounds of LLM Reasoning: Are Multi-Agent Discussions the Key?
Qineng Wang, Zihao Wang, Ying Su et al. · Language models & NLP
Recent progress in LLMs discussion suggests that multi-agent discussion improves the reasoning abilities of LLMs. In this work, we reevaluate this claim through systematic experiments, where we propose a novel group discussion framework to enrich the set of discussion mechanisms.
- 2024-02arXiv 2402.17400Investigating Continual Pretraining in Large Language Models: Insights and Implications
Çağatay Yıldız, Nishaanth Kanna Ravichandran, Nitin Sharma et al. · Language models & NLP
Continual learning (CL) in large language models (LLMs) is an evolving domain that focuses on developing efficient and sustainable training strategies to adapt models to emerging knowledge and achieve robustness in dynamic environments.
- 2024-02arXiv 2402.17753Evaluating Very Long-Term Conversational Memory of LLM Agents
Adyasha Maharana, Dong-Ho Lee, Sergey Tulyakov et al. · Language models & NLP
Existing works on long-term open-domain dialogues focus on evaluating model responses within contexts spanning no more than five chat sessions.
- 2024-02arXiv 2402.16817Investigating the Effectiveness of HyperTuning via Gisting
Jason Phang · Language models & NLP
, 2023) is a simple method for training models to compress information into fewer token representations using a modified attention mask, and can serve as an economical approach to training Transformer-based hypernetworks.
- 2024-02arXiv 2402.16823Language Agents as Optimizable Graphs
Mingchen Zhuge, Wenyi Wang, Louis Kirsch et al. · AI & agents
Various human-designed prompt engineering techniques have been proposed to improve problem solvers based on Large Language Models (LLMs), yielding many disparate code bases.
- 2024-02arXiv 2402.14800Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models
Xudong Lu, Qi Liu, Yuhui Xu et al. · Language models & NLP
A pivotal advancement in the progress of large language models (LLMs) is the emergence of the Mixture-of-Experts (MoE) LLMs. Compared to traditional LLMs, MoE LLMs can achieve higher performance with fewer parameters, but it is still hard to deploy them due to their immense parameter sizes.
- 2024-02arXiv 2402.14903Tokenization counts: the impact of tokenization on arithmetic in frontier LLMs
Aaditya K. Singh, DJ Strouse · Language models & NLP
Tokenization, the division of input text into input tokens, is an often overlooked aspect of the large language model (LLM) pipeline and could be the source of useful or harmful inductive biases.
Cited inWhat Language Buys - 2024-02arXiv 2402.14992tinyBenchmarks: evaluating LLMs with fewer examples
Felipe Maia Polo, Lucas Weber, Leshem Choshen et al. · Language models & NLP
The versatility of large language models (LLMs) led to the creation of diverse benchmarks that thoroughly test a variety of language models' abilities. These benchmarks consist of tens of thousands of examples making evaluation of LLMs very expensive.
- 2024-02arXiv 2402.13494GradSafe: Detecting Jailbreak Prompts for LLMs via Safety-Critical Gradient Analysis
Yueqi Xie, Minghong Fang, Renjie Pi et al. · Language models & NLP
Large Language Models (LLMs) face threats from jailbreak prompts. Existing methods for detecting jailbreak prompts are primarily online moderation APIs or finetuned LLMs.
- 2024-02arXiv 2402.11746Language Models are Homer Simpson! Safety Re-Alignment of Fine-tuned Language Models through Task Arithmetic
Rishabh Bhardwaj, Do Duc Anh, Soujanya Poria · Language models & NLP
Aligned language models face a significant limitation as their fine-tuning often results in compromised safety. To tackle this, we propose a simple method RESTA that performs LLM safety realignment.
- 2024-02arXiv 2402.11782What Evidence Do Language Models Find Convincing?
Alexander Wan, Eric Wallace, Dan Klein · Language models & NLP
Retrieval-augmented language models are being increasingly tasked with subjective, contentious, and conflicting queries such as "is aspartame linked to cancer".".
- 2024-02arXiv 2402.12048Model Tailor: Mitigating Catastrophic Forgetting in Multi-modal Large Language Models
Didi Zhu, Zhongyi Sun, Zexi Li et al. · Language models & NLP
Catastrophic forgetting emerges as a critical challenge when fine-tuning multi-modal large language models (MLLMs), where improving performance on unseen tasks often leads to a significant performance drop on the original tasks.
- 2024-02arXiv 2402.12366A Critical Evaluation of AI Feedback for Aligning Large Language Models
Archit Sharma, Sedrick Keh, Eric Mitchell et al. · Machine learning
Reinforcement learning with AI feedback (RLAIF) is a popular paradigm for improving the instruction-following abilities of powerful pre-trained language models.
- 2024-02arXiv 2402.11655Competition of Mechanisms: Tracing How Language Models Handle Facts and Counterfactuals
Francesco Ortu, Zhijing Jin, Diego Doimo et al. · Language models & NLP
Interpretability research aims to bridge the gap between empirical success and our scientific understanding of the inner workings of large language models (LLMs).
- 2024-02arXiv 2402.09997LoraRetriever: Input-Aware LoRA Retrieval and Composition for Mixed Tasks in the Wild
Ziyu Zhao, Leilei Gan, Guoyin Wang et al. · AI & agents
Low-Rank Adaptation (LoRA) provides an effective yet efficient solution for fine-tuning large language models (LLM). The modular and plug-and-play nature of LoRA enables the integration of diverse domain-specific LoRAs to enhance the capabilities of LLMs.
- 2024-02arXiv 2402.10058Towards Safer Large Language Models through Machine Unlearning
Zheyuan Liu, Guangyao Dou, Zhaoxuan Tan et al. · Language models & NLP
The rapid advancement of Large Language Models (LLMs) has demonstrated their vast potential across various domains, attributed to their extensive pretraining knowledge and exceptional generalizability.
- 2024-02arXiv 2402.10978Language Models with Conformal Factuality Guarantees
Christopher Mohri, Tatsunori Hashimoto · Machine learning
Guaranteeing the correctness and factuality of language model (LM) outputs is a major open problem. In this work, we propose conformal factuality, a framework that can ensure high probability correctness guarantees for LMs by connecting language modeling and conformal prediction.
- 2024-02arXiv 2402.07510Secret Collusion among AI Agents: Multi-Agent Deception via Steganography
Sumeet Ramesh Motwani, Mikhail Baranchuk, Martin Strohmeier et al. · AI & agents
Recent capability increases in large language models (LLMs) open up applications in which groups of communicating generative AI agents solve joint tasks. This poses privacy and security challenges concerning the unauthorised sharing of information, or other unwanted forms of agent coordination.
- 2024-02arXiv 2402.07867PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language Models
Wei Zou, Runpeng Geng, Binghui Wang et al. · Security
Large language models (LLMs) have achieved remarkable success due to their exceptional generative capabilities. Despite their success, they also have inherent limitations such as a lack of up-to-date knowledge and hallucination.
- 2024-02arXiv 2402.07871Scaling Laws for Fine-Grained Mixture of Experts
Jakub Krajewski, Jan Ludziejewski, Kamil Adamczewski et al. · Machine learning
Mixture of Experts (MoE) models have emerged as a primary solution for reducing the computational cost of Large Language Models. In this work, we analyze their scaling properties, incorporating an expanded range of variables.
- 2024-02arXiv 2402.07043A Tale of Tails: Model Collapse as a Change of Scaling Laws
Elvis Dohmatob, Yunzhen Feng, Pu Yang et al. · ICML 2024 · Machine learning
As AI model size grows, neural scaling laws have become a crucial tool to predict the improvements of large models when increasing capacity and the size of original (human or natural) training data.
- 2024-02arXiv 2402.06782Debating with More Persuasive LLMs Leads to More Truthful Answers
Akbir Khan, John Hughes, Dan Valentine et al. · AI & agents
Common methods for aligning large language models (LLMs) with desired behaviour heavily rely on human-labelled data. However, as models grow increasingly sophisticated, they will surpass human expertise, and the role of human evaluation will evolve into non-experts overseeing experts.
- 2024-02arXiv 2402.05859Learning to Route Among Specialized Experts for Zero-Shot Generalization
Mohammed Muqeeth, Haokun Liu, Yufan Liu et al. · Machine learning
Recently, there has been a widespread proliferation of "expert" language models that are specialized to a specific task or domain through parameter-efficient fine-tuning.
- 2024-02arXiv 2402.03744INSIDE: LLMs' Internal States Retain the Power of Hallucination Detection
Chao Chen, Kai Liu, Ze Chen et al. · Language models & NLP
Knowledge hallucination have raised widespread concerns for the security and reliability of deployed LLMs.
- 2024-02arXiv 2402.04333LESS: Selecting Influential Data for Targeted Instruction Tuning
Mengzhou Xia, Sadhika Malladi, Suchin Gururangan et al. · Language models & NLP
Instruction tuning has unlocked powerful capabilities in large language models (LLMs), effectively using combined datasets to develop generalpurpose chatbots., reasoning).
- 2024-02arXiv 2402.05120More Agents Is All You Need
Junyou Li, Qin Zhang, Yangbin Yu et al. · Language models & NLP
We find that, simply via a sampling-and-voting method, the performance of large language models (LLMs) scales with the number of agents instantiated.
- 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.
- 2024-02arXiv 2402.01830PiCO: Peer Review in LLMs based on the Consistency Optimization
Kun-Peng Ning, Shuo Yang, Yu-Yang Liu et al. · Language models & NLP
Existing large language models (LLMs) evaluation methods typically focus on testing the performance on some closed-environment and domain-specific benchmarks with human annotations.
- 2024-02arXiv 2402.01865What Will My Model Forget? Forecasting Forgotten Examples in Language Model Refinement
Xisen Jin, Xiang Ren · Machine learning
Language models deployed in the wild make errors. However, simply updating the model with the corrected error instances causes catastrophic forgetting -- the updated model makes errors on instances learned during the instruction tuning or upstream training phase.
Cited inA gradient kernel flags contradictions but does not predict forgetting, and steering rehearsal by it backfiresCoherence is selected, anchored, then pruned: a literature synthesis on logical coherence in LLM world modelsTo stop a model forgetting, rehearse what it knew. New context won't do it.Study: Continual learning order and companion dataStudy: Decision signatures for conflict detectionStudy: Logical coherence of LLM world modelsStudy: Seven continual learning methods tested - 2024-02arXiv 2402.00367Don't Hallucinate, Abstain: Identifying LLM Knowledge Gaps via Multi-LLM Collaboration
Shangbin Feng, Weijia Shi, Yike Wang et al. · Language models & NLP
Despite efforts to expand the knowledge of large language models (LLMs), knowledge gaps -- missing or outdated information in LLMs -- might always persist given the evolving nature of knowledge.
- 2024-01arXiv 2401.16646Incoherent Probability Judgments in Large Language Models
Jian-Qiao Zhu, Thomas L. Griffiths · CogSci 2024 · Language models & NLP
Autoregressive Large Language Models (LLMs) trained for next-word prediction have demonstrated remarkable proficiency at producing coherent text. But are they equally adept at forming coherent probability judgments?
- 2024-01arXiv 2401.16650Augmenting Replay in World Models for Continual Reinforcement Learning
Luke Yang, Levin Kuhlmann, Gideon Kowadlo · Machine learning
Continual RL requires an agent to learn new tasks without forgetting previous ones, while improving on both past and future tasks.
Cited inStudy: Genesys program prior art - 2024-01arXiv 2401.16386Continual Learning with Pre-Trained Models: A Survey
Da-Wei Zhou, Hai-Long Sun, Jingyi Ning et al. · Machine learning
Nowadays, real-world applications often face streaming data, which requires the learning system to absorb new knowledge as data evolves. Continual Learning (CL) aims to achieve this goal and meanwhile overcome the catastrophic forgetting of former knowledge when learning new ones.
- 2024-01arXiv 2402.01739OpenMoE: An Early Effort on Open Mixture-of-Experts Language Models
Fuzhao Xue, Zian Zheng, Yao Fu et al. · Language models & NLP
To help the open-source community have a better understanding of Mixture-of-Experts (MoE) based large language models (LLMs), we train and release OpenMoE, a series of fully open-sourced and reproducible decoder-only MoE LLMs, ranging from 650M to 34B parameters and trained on up to over 1T tokens.
- 2024-01arXiv 2401.14885Neuromorphic quadratic programming for efficient and scalable model predictive control
Ashish Rao Mangalore, Gabriel Andres Fonseca Guerra, Sumedh R. Risbud et al. · Neuroscience & neural computation
Applications in robotics or other size-, weight- and power-constrained autonomous systems at the edge often require real-time and low-energy solutions to large optimization problems.
Built from every arXiv paper cited in our papers, posts, topic hubs, reports and research notes, with titles, authors, dates and abstracts from the arXiv API (updated 2026-11-12). Summaries are the opening sentences of each paper's own abstract, not our assessment; a citation means we used or checked the work, not that we endorse it. Fields follow each paper's primary arXiv category. Papers cited only outside arXiv (journals, proceedings) are not listed yet. Entries are generated from our citations, so a mistyped ID in a note would list the wrong paper; we remove those as we find them. Report a wrong entry.
