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.
- 2024-04arXiv 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.
- 2024-04arXiv 2404.04475Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators
Yann Dubois, Balázs Galambosi, Percy Liang et al. · Machine learning
LLM-based auto-annotators have become a key component of the LLM development process due to their cost-effectiveness and scalability compared to human-based evaluation.
Cited inStudy: Genesys program prior art - 2024-04arXiv 2404.03592ReFT: Representation Finetuning for Language Models
Zhengxuan Wu, Aryaman Arora, Zheng Wang et al. · Language models & NLP
Parameter-efficient finetuning (PEFT) methods seek to adapt large neural models via updates to a small number of weights.
- 2024-04arXiv 2404.04286Bias Amplification in Language Model Evolution: An Iterated Learning Perspective
Yi Ren, Shangmin Guo, Linlu Qiu et al. · Language models & NLP
With the widespread adoption of Large Language Models (LLMs), the prevalence of iterative interactions among these models is anticipated to increase. Notably, recent advancements in multi-round self-improving methods allow LLMs to generate new examples for training subsequent models.
- 2024-04arXiv 2405.01563Mitigating LLM Hallucinations via Conformal Abstention
Yasin Abbasi Yadkori, Ilja Kuzborskij, David Stutz et al. · Machine learning
, by saying "I don't know") in a general domain, instead of resorting to possibly "hallucinating" a non-sensical or incorrect answer.
- 2024-04arXiv 2404.02642Urban Scaling Laws
Fabiano L. Ribeiro, Vinicius M. Netto · Hardware, photonics & physics
Understanding how size influences the internal characteristics of a system is a crucial concern across various fields. Concepts like scale invariance, universalities, and fractals are fundamental to this inquiry and find application in biology, physics, and particularly urbanism.
- 2024-04arXiv 2404.02258Mixture-of-Depths: Dynamically allocating compute in transformer-based language models
David Raposo, Sam Ritter, Blake Richards et al. · Machine learning
Transformer-based language models spread FLOPs uniformly across input sequences.
- 2024-04arXiv 2404.01019Source-Aware Training Enables Knowledge Attribution in Language Models
Muhammad Khalifa, David Wadden, Emma Strubell et al. · Language models & NLP
Large language models (LLMs) learn a vast amount of knowledge during pretraining, but they are often oblivious to the source(s) of such knowledge. We investigate the problem of intrinsic source citation, where LLMs are required to cite the pretraining source supporting a generated response.
- 2024-04arXiv 2404.01413Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data
Matthias Gerstgrasser, Rylan Schaeffer, Apratim Dey et al. · Machine learning
The proliferation of generative models, combined with pretraining on web-scale data, raises a timely question: what happens when these models are trained on their own generated outputs?
- 2024-03arXiv 2404.00806Algorithmic Collusion by Large Language Models
Sara Fish, Yannai A. Gonczarowski, Ran I. Shorrer · Society, software & economics
We conduct experiments with algorithmic pricing agents based on Large Language Models (LLMs). In oligopoly settings, LLM-based pricing agents quickly and autonomously reach supracompetitive prices and profits.
- 2024-03arXiv 2404.00228InfLoRA: Interference-Free Low-Rank Adaptation for Continual Learning
Yan-Shuo Liang, Wu-Jun Li · Machine learning
Continual learning requires the model to learn multiple tasks sequentially. In continual learning, the model should possess the ability to maintain its performance on old tasks (stability) and the ability to adapt to new tasks continuously (plasticity).
- 2024-03arXiv 2403.20279LUQ: Long-text Uncertainty Quantification for LLMs
Caiqi Zhang, Fangyu Liu, Marco Basaldella et al. · Language models & NLP
Large Language Models (LLMs) have demonstrated remarkable capability in a variety of NLP tasks. However, LLMs are also prone to generate nonfactual content.
- 2024-03arXiv 2403.18802Long-form factuality in large language models
Jerry Wei, Chengrun Yang, Xinying Song et al. · Language models & NLP
Large language models (LLMs) often generate content that contains factual errors when responding to fact-seeking prompts on open-ended topics.
- 2024-03arXiv 2403.18886Self-Expansion of Pre-trained Models with Mixture of Adapters for Continual Learning
Huiyi Wang, Haodong Lu, Lina Yao et al. · Machine learning
Continual learning (CL) aims to continually accumulate knowledge from a non-stationary data stream without catastrophic forgetting of learned knowledge, requiring a balance between stability and adaptability.
- 2024-03arXiv 2403.17806Have Faith in Faithfulness: Going Beyond Circuit Overlap When Finding Model Mechanisms
Michael Hanna, Sandro Pezzelle, Yonatan Belinkov · Machine learning
Many recent language model (LM) interpretability studies have adopted the circuits framework, which aims to find the minimal computational subgraph, or circuit, that explains LM behavior on a given task.
- 2024-03arXiv 2403.14236A Unified Framework for Model Editing
Akshat Gupta, Dev Sajnani, Gopala Anumanchipalli · Machine learning
ROME and MEMIT are largely believed to be two different model editing algorithms, with the major difference between them being the ability to perform batched edits.
- 2024-03arXiv 2403.14472Detoxifying Large Language Models via Knowledge Editing
Mengru Wang, Ningyu Zhang, Ziwen Xu et al. · Language models & NLP
This paper investigates using knowledge editing techniques to detoxify Large Language Models (LLMs). We construct a benchmark, SafeEdit, which covers nine unsafe categories with various powerful attack prompts and equips comprehensive metrics for systematic evaluation.
- 2024-03arXiv 2403.13257Arcee's MergeKit: A Toolkit for Merging Large Language Models
Charles Goddard, Shamane Siriwardhana, Malikeh Ehghaghi et al. · Language models & NLP
The rapid expansion of the open-source language model landscape presents an opportunity to merge the competencies of these model checkpoints by combining their parameters.
- 2024-03arXiv 2403.13355BadEdit: Backdooring large language models by model editing
Yanzhou Li, Tianlin Li, Kangjie Chen et al. · Security
Mainstream backdoor attack methods typically demand substantial tuning data for poisoning, limiting their practicality and potentially degrading the overall performance when applied to Large Language Models (LLMs).
- 2024-03arXiv 2403.12958Dated Data: Tracing Knowledge Cutoffs in Large Language Models
Jeffrey Cheng, Marc Marone, Orion Weller et al. · Language models & NLP
Released Large Language Models (LLMs) are often paired with a claimed knowledge cutoff date, or the dates at which training data was gathered. Such information is crucial for applications where the LLM must provide up to date information.
- 2024-03arXiv 2403.13187Evolutionary Optimization of Model Merging Recipes
Takuya Akiba, Makoto Shing, Yujin Tang et al. · Nat Mach Intell (2025) · Neuroscience & neural computation
Large language models (LLMs) have become increasingly capable, but their development often requires substantial computational resources.
- 2024-03arXiv 2403.11549Boosting Continual Learning of Vision-Language Models via Mixture-of-Experts Adapters
Jiazuo Yu, Yunzhi Zhuge, Lu Zhang et al. · Vision
Continual learning can empower vision-language models to continuously acquire new knowledge, without the need for access to the entire historical dataset.
- 2024-03arXiv 2403.11901Larimar: Large Language Models with Episodic Memory Control
Payel Das, Subhajit Chaudhury, Elliot Nelson et al. · Machine learning
Efficient and accurate updating of knowledge stored in Large Language Models (LLMs) is one of the most pressing research challenges today. This paper presents Larimar - a novel, brain-inspired architecture for enhancing LLMs with a distributed episodic memory.
- 2024-03arXiv 2403.12031RouterBench: A Benchmark for Multi-LLM Routing System
Qitian Jason Hu, Jacob Bieker, Xiuyu Li et al. · Machine learning
As the range of applications for Large Language Models (LLMs) continues to grow, the demand for effective serving solutions becomes increasingly critical.
- 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 2403.08350CoIN: A Benchmark of Continual Instruction tuNing for Multimodel Large Language Model
Cheng Chen, Junchen Zhu, Xu Luo et al. · Vision
Instruction tuning represents a prevalent strategy employed by Multimodal Large Language Models (MLLMs) to align with human instructions and adapt to new tasks. Nevertheless, MLLMs encounter the challenge of adapting to users' evolving knowledge and demands.
- 2024-03arXiv 2403.08568Consistent Prompting for Rehearsal-Free Continual Learning
Zhanxin Gao, Jun Cen, Xiaobin Chang · Vision
Continual learning empowers models to adapt autonomously to the ever-changing environment or data streams without forgetting old knowledge. Prompt-based approaches are built on frozen pre-trained models to learn the task-specific prompts and classifiers efficiently.
- 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 2403.08882Cultural evolution in populations of Large Language Models
Jérémy Perez, Corentin Léger, Marcela Ovando-Tellez et al. · AI & agents
Research in cultural evolution aims at providing causal explanations for the change of culture over time. Over the past decades, this field has generated an important body of knowledge, using experimental, historical, and computational methods.
- 2024-03arXiv 2403.08994Ethos: Rectifying Language Models in Orthogonal Parameter Space
Lei Gao, Yue Niu, Tingting Tang et al. · Language models & NLP
Language models (LMs) have greatly propelled the research on natural language processing. However, LMs also raise concerns regarding the generation of biased or toxic content and the potential disclosure of private information from the training dataset.
- 2024-03arXiv 2403.10557Second-Order Information Matters: Revisiting Machine Unlearning for Large Language Models
Kang Gu, Md Rafi Ur Rashid, Najrin Sultana et al. · Machine learning
With the rapid development of Large Language Models (LLMs), we have witnessed intense competition among the major LLM products like ChatGPT, LLaMa, and Gemini. privacy leakage and copyright violation) of the training corpus still remain underexplored.
- 2024-03arXiv 2403.07652Harder Tasks Need More Experts: Dynamic Routing in MoE Models
Quzhe Huang, Zhenwei An, Nan Zhuang et al. · Machine learning
In this paper, we introduce a novel dynamic expert selection framework for Mixture of Experts (MoE) models, aiming to enhance computational efficiency and model performance by adjusting the number of activated experts based on input difficulty.
- 2024-03arXiv 2403.07714StableToolBench: Towards Stable Large-Scale Benchmarking on Tool Learning of Large Language Models
Zhicheng Guo, Sijie Cheng, Hao Wang et al. · Language models & NLP
Large Language Models (LLMs) have witnessed remarkable advancements in recent years, prompting the exploration of tool learning, which integrates LLMs with external tools to address diverse real-world challenges.
Cited inStudy: Genesys program prior art - 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 2403.07175Rebuilding ROME : Resolving Model Collapse during Sequential Model Editing
Akshat Gupta, Sidharth Baskaran, Gopala Anumanchipalli · Language models & NLP
Recent work using Rank-One Model Editing (ROME), a popular model editing method, has shown that there are certain facts that the algorithm is unable to edit without breaking the model.
- 2024-03arXiv 2403.05612Unfamiliar Finetuning Examples Control How Language Models Hallucinate
Katie Kang, Eric Wallace, Claire Tomlin et al. · Machine learning
Large language models are known to hallucinate when faced with unfamiliar queries, but the underlying mechanism that govern how models hallucinate are not yet fully understood.
- 2024-03arXiv 2403.04132Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference
Wei-Lin Chiang, Lianmin Zheng, Ying Sheng et al. · AI & agents
Large Language Models (LLMs) have unlocked new capabilities and applications; however, evaluating the alignment with human preferences still poses significant challenges.
- 2024-03arXiv 2403.03208Active Statistical Inference
Tijana Zrnic, Emmanuel J. Candès · Machine learning
Inspired by the concept of active learning, we propose active inference$\unicode{x2013}$a methodology for statistical inference with machine-learning-assisted data collection.
Cited inInternal notes - 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.
- 2024-03arXiv 2403.02419Are More LLM Calls All You Need? Towards Scaling Laws of Compound Inference Systems
Lingjiao Chen, Jared Quincy Davis, Boris Hanin et al. · Machine learning
Many recent state-of-the-art results in language tasks were achieved using compound systems that perform multiple Language Model (LM) calls and aggregate their responses.
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.
