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.
- 2024-06arXiv 2406.01297When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMs
Ryo Kamoi, Yusen Zhang, Nan Zhang et al. · Transactions of the Association for Computational Linguistics (2024) 12: 1417-1440 · Language models & NLP
Self-correction is an approach to improving responses from large language models (LLMs) by refining the responses using LLMs during inference. Prior work has proposed various self-correction frameworks using different sources of feedback, including self-evaluation and external feedback.
- 2024-06arXiv 2406.01436Editing the Mind of Giants: An In-Depth Exploration of Pitfalls of Knowledge Editing in Large Language Models
Cheng-Hsun Hsueh, Paul Kuo-Ming Huang, Tzu-Han Lin et al. · Language models & NLP
Knowledge editing is a rising technique for efficiently updating factual knowledge in large language models (LLMs) with minimal alteration of parameters.
- 2024-06arXiv 2406.00799Get my drift? Catching LLM Task Drift with Activation Deltas
Sahar Abdelnabi, Aideen Fay, Giovanni Cherubin et al. · Security
LLMs are commonly used in retrieval-augmented applications to execute user instructions based on data from external sources. For example, modern search engines use LLMs to answer queries based on relevant search results; email plugins summarize emails by processing their content through an LLM.
- 2024-06arXiv 2406.06560Inverse Constitutional AI: Compressing Preferences into Principles
Arduin Findeis, Timo Kaufmann, Eyke Hüllermeier et al. · Language models & NLP
Feedback data is widely used for fine-tuning and evaluating state-of-the-art AI models. Pairwise text preferences, where human or AI annotators select the "better" of two options, are particularly common.
- 2024-06arXiv 2406.00392Artificial Generational Intelligence: Cultural Accumulation in Reinforcement Learning
Jonathan Cook, Chris Lu, Edward Hughes et al. · AI & agents
Cultural accumulation drives the open-ended and diverse progress in capabilities spanning human history. It builds an expanding body of knowledge and skills by combining individual exploration with inter-generational information transmission.
- 2024-05arXiv 2405.21030Standards for Belief Representations in LLMs
Daniel A. Herrmann, Benjamin A. Levinstein · Minds and Machines 35:5 (2025) 1-25 · AI & agents
As large language models (LLMs) continue to demonstrate remarkable abilities across various domains, computer scientists are developing methods to understand their cognitive processes, particularly concerning how (and if) LLMs internally represent their beliefs about the world.
- 2024-05arXiv 2405.20003Kernel Language Entropy: Fine-grained Uncertainty Quantification for LLMs from Semantic Similarities
Alexander Nikitin, Jannik Kossen, Yarin Gal et al. · Machine learning
Uncertainty quantification in Large Language Models (LLMs) is crucial for applications where safety and reliability are important. In particular, uncertainty can be used to improve the trustworthiness of LLMs by detecting factually incorrect model responses, commonly called hallucinations.
- 2024-05arXiv 2405.18432Unsupervised Model Tree Heritage Recovery
Eliahu Horwitz, Asaf Shul, Yedid Hoshen · Machine learning
The number of models shared online has recently skyrocketed, with over one million public models available on Hugging Face. Sharing models allows other users to build on existing models, using them as initialization for fine-tuning, improving accuracy, and saving compute and energy.
- 2024-05arXiv 2405.16039MoEUT: Mixture-of-Experts Universal Transformers
Róbert Csordás, Kazuki Irie, Jürgen Schmidhuber et al. · Machine learning
Previous work on Universal Transformers (UTs) has demonstrated the importance of parameter sharing across layers.
- 2024-05arXiv 2405.15568OMNI-EPIC: Open-endedness via Models of human Notions of Interestingness with Environments Programmed in Code
Maxence Faldor, Jenny Zhang, Antoine Cully et al. · AI & agents
Open-ended and AI-generating algorithms aim to continuously generate and solve increasingly complex tasks indefinitely, offering a promising path toward more general intelligence.
- 2024-05arXiv 2405.14117Knowledge Localization: Mission Not Accomplished? Enter Query Localization!
Yuheng Chen, Pengfei Cao, Yubo Chen et al. · Language models & NLP
Large language models (LLMs) store extensive factual knowledge, but the mechanisms behind how they store and express this knowledge remain unclear. The Knowledge Neuron (KN) thesis is a prominent theory for explaining these mechanisms.
- 2024-05arXiv 2405.14297Dynamic Mixture of Experts: An Auto-Tuning Approach for Efficient Transformer Models
Yongxin Guo, Zhenglin Cheng, Xiaoying Tang et al. · Machine learning
However, the performance of SMoE heavily depends on the choice of hyper-parameters, such as the number of experts and the number of experts to be activated (referred to as top-k), resulting in significant computational overhead due to the extensive model training by searching over various hyper-parameter…
- 2024-05arXiv 2405.14577Representation Noising: A Defence Mechanism Against Harmful Finetuning
Domenic Rosati, Jan Wehner, Kai Williams et al. · Language models & NLP
Releasing open-source large language models (LLMs) presents a dual-use risk since bad actors can easily fine-tune these models for harmful purposes. Even without the open release of weights, weight stealing and fine-tuning APIs make closed models vulnerable to harmful fine-tuning attacks (HFAs).
- 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 2405.14831HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models
Bernal Jiménez Gutiérrez, Yiheng Shu, Yu Gu et al. · Language models & NLP
In order to thrive in hostile and ever-changing natural environments, mammalian brains evolved to store large amounts of knowledge about the world and continually integrate new information while avoiding catastrophic forgetting.
Cited inStudy: Graphyte spec - 2024-05arXiv 2405.13954What is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions
Sang Keun Choe, Hwijeen Ahn, Juhan Bae et al. · Machine learning
Large language models (LLMs) are trained on a vast amount of human-written data, but data providers often remain uncredited.
- 2024-05arXiv 2405.15815A social path to human-like artificial intelligence
Edgar A. Duéñez-Guzmán, Suzanne Sadedin, Jane X. Wang et al. · AI & agents
Traditionally, cognitive and computer scientists have viewed intelligence solipsistically, as a property of unitary agents devoid of social context.
- 2024-05arXiv 2405.11613Decoding by Contrasting Knowledge: Enhancing LLMs' Confidence on Edited Facts
Baolong Bi, Shenghua Liu, Lingrui Mei et al. · Language models & NLP
The knowledge within large language models (LLMs) may become outdated quickly. While in-context editing (ICE) is currently the most effective method for knowledge editing (KE), it is constrained by the black-box modeling of LLMs and thus lacks interpretability.
- 2024-05arXiv 2405.11157Towards Modular LLMs by Building and Reusing a Library of LoRAs
Oleksiy Ostapenko, Zhan Su, Edoardo Maria Ponti et al. · Machine learning
The growing number of parameter-efficient adaptations of a base large language model (LLM) calls for studying whether we can reuse such trained adapters to improve performance for new tasks.
- 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 2405.07518SambaNova SN40L: Scaling the AI Memory Wall with Dataflow and Composition of Experts
Raghu Prabhakar, Ram Sivaramakrishnan, Darshan Gandhi et al. · Hardware, photonics & physics
Monolithic large language models (LLMs) like GPT-4 have paved the way for modern generative AI applications. Training, serving, and maintaining monolithic LLMs at scale, however, remains prohibitively expensive and challenging.
- 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 - 2024-05arXiv 2405.04434DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model
DeepSeek-AI, Aixin Liu, Bei Feng et al. · Language models & NLP
We present DeepSeek-V2, a strong Mixture-of-Experts (MoE) language model characterized by economical training and efficient inference. It comprises 236B total parameters, of which 21B are activated for each token, and supports a context length of 128K tokens.
- 2024-05arXiv 2405.03133Lory: Fully Differentiable Mixture-of-Experts for Autoregressive Language Model Pre-training
Zexuan Zhong, Mengzhou Xia, Danqi Chen et al. · Language models & NLP
Mixture-of-experts (MoE) models facilitate efficient scaling; however, training the router network introduces the challenge of optimizing a non-differentiable, discrete objective.
- 2024-05arXiv 2405.02421What does the Knowledge Neuron Thesis Have to do with Knowledge?
Jingcheng Niu, Andrew Liu, Zining Zhu et al. · Language models & NLP
We reassess the Knowledge Neuron (KN) Thesis: an interpretation of the mechanism underlying the ability of large language models to recall facts from a training corpus.
- 2024-05arXiv 2405.00332A Careful Examination of Large Language Model Performance on Grade School Arithmetic
Hugh Zhang, Jeff Da, Dean Lee et al. · Language models & NLP
Large language models (LLMs) have achieved impressive success on many benchmarks for mathematical reasoning.
- 2024-04arXiv 2404.18466HFT: Half Fine-Tuning for Large Language Models
Tingfeng Hui, Zhenyu Zhang, Shuohuan Wang et al. · Language models & NLP
Large language models (LLMs) with one or more fine-tuning phases have become a necessary step to unlock various capabilities, enabling LLMs to follow natural language instructions or align with human preferences.
- 2024-04arXiv 2404.16698Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents
Giorgio Piatti, Zhijing Jin, Max Kleiman-Weiner et al. · Language models & NLP
As AI systems pervade human life, ensuring that large language models (LLMs) make safe decisions remains a significant challenge.
- 2024-04arXiv 2404.16767REBEL: Reinforcement Learning via Regressing Relative Rewards
Zhaolin Gao, Jonathan D. Chang, Wenhao Zhan et al. · Machine learning
While originally developed for continuous control problems, Proximal Policy Optimization (PPO) has emerged as the work-horse of a variety of reinforcement learning (RL) applications, including the fine-tuning of generative models.
- 2024-04arXiv 2404.16130From Local to Global: A Graph RAG Approach to Query-Focused Summarization
Darren Edge, Ha Trinh, Newman Cheng et al. · Language models & NLP
The use of retrieval-augmented generation (RAG) to retrieve relevant information from an external knowledge source enables large language models (LLMs) to answer questions over private and/or previously unseen document collections.
- 2024-04arXiv 2404.15045Multi-Head Mixture-of-Experts
Xun Wu, Shaohan Huang, Wenhui Wang et al. · Language models & NLP
Sparse Mixtures of Experts (SMoE) scales model capacity without significant increases in training and inference costs, but exhibits the following two issues: (1) Low expert activation, where only a small subset of experts are activated for optimization.
- 2024-04arXiv 2404.14394A Multimodal Automated Interpretability Agent
Tamar Rott Shaham, Sarah Schwettmann, Franklin Wang et al. · AI & agents
This paper describes MAIA, a Multimodal Automated Interpretability Agent. MAIA is a system that uses neural models to automate neural model understanding tasks like feature interpretation and failure mode discovery.
- 2024-04arXiv 2404.14618Hybrid LLM: Cost-Efficient and Quality-Aware Query Routing
Dujian Ding, Ankur Mallick, Chi Wang et al. · Machine learning
, edge) devices, tend to lag behind in terms of response quality. Therefore in this work we propose a hybrid inference approach which combines their respective strengths to save cost and maintain quality.
- 2024-04arXiv 2404.13628Mixture of LoRA Experts
Xun Wu, Shaohan Huang, Furu Wei · Language models & NLP
LoRA has gained widespread acceptance in the fine-tuning of large pre-trained models to cater to a diverse array of downstream tasks, showcasing notable effectiveness and efficiency, thereby solidifying its position as one of the most prevalent fine-tuning techniques.
- 2024-04arXiv 2404.13208The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions
Eric Wallace, Kai Xiao, Reimar Leike et al. · Security
Today's LLMs are susceptible to prompt injections, jailbreaks, and other attacks that allow adversaries to overwrite a model's original instructions with their own malicious prompts.
- 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 2404.09523More, better or different? Trade-offs between group size and competence development in jury theorems
Gustaf Arrhenius, Klas Markström · Society, software & economics
In many circumstances there is a trade off between the number of voters and the time they can be given before having to make a decision since both aspects are costly.
- 2024-04arXiv 2404.10136Language Model Cascades: Token-level uncertainty and beyond
Neha Gupta, Harikrishna Narasimhan, Wittawat Jitkrittum et al. · Language models & NLP
Recent advances in language models (LMs) have led to significant improvements in quality on complex NLP tasks, but at the expense of increased inference costs.
- 2024-04arXiv 2404.13076LLM Evaluators Recognize and Favor Their Own Generations
Arjun Panickssery, Samuel R. Bowman, Shi Feng · Language models & NLP
Self-evaluation using large language models (LLMs) has proven valuable not only in benchmarking but also methods like reward modeling, constitutional AI, and self-refinement.
- 2024-04arXiv 2404.06809Not All Contexts Are Equal: Teaching LLMs Credibility-aware Generation
Ruotong Pan, Boxi Cao, Hongyu Lin et al. · Language models & NLP
The rapid development of large language models has led to the widespread adoption of Retrieval-Augmented Generation (RAG), which integrates external knowledge to alleviate knowledge bottlenecks and mitigate hallucinations.
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.
