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.
- 2023-05arXiv 2305.13300Adaptive Chameleon or Stubborn Sloth: Revealing the Behavior of Large Language Models in Knowledge Conflicts
Jian Xie, Kai Zhang, Jiangjie Chen et al. · Language models & NLP
By providing external information to large language models (LLMs), tool augmentation (including retrieval augmentation) has emerged as a promising solution for addressing the limitations of LLMs' static parametric memory.
Cited inCoherence is selected, anchored, then pruned: a literature synthesis on logical coherence in LLM world modelsSuccessors should earn authority, not win contests: graded succession in continual learningStudy: Decision signatures for conflict detectionStudy: Graded succession by earned authorityStudy: Logical coherence of LLM world modelsStudy: Memory world models and source trustStudy: Model self knowledge and confident errors - 2023-05arXiv 2305.12281Lifelong Language Pretraining with Distribution-Specialized Experts
Wuyang Chen, Yanqi Zhou, Nan Du et al. · Language models & NLP
Pretraining on a large-scale corpus has become a standard method to build general language models (LMs). Adapting a model to new data distributions targeting different downstream tasks poses significant challenges.
- 2023-05arXiv 2305.12295Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning
Liangming Pan, Alon Albalak, Xinyi Wang et al. · Language models & NLP
Large Language Models (LLMs) have shown human-like reasoning abilities but still struggle with complex logical problems. This paper introduces a novel framework, Logic-LM, which integrates LLMs with symbolic solvers to improve logical problem-solving.
- 2023-05arXiv 2305.11171TrueTeacher: Learning Factual Consistency Evaluation with Large Language Models
Zorik Gekhman, Jonathan Herzig, Roee Aharoni et al. · Language models & NLP
Factual consistency evaluation is often conducted using Natural Language Inference (NLI) models, yet these models exhibit limited success in evaluating summaries.
- 2023-05arXiv 2305.10429DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining
Sang Michael Xie, Hieu Pham, Xuanyi Dong et al. · Language models & NLP
, Wikipedia, books, web text) greatly affect language model (LM) performance.
- 2023-05arXiv 2305.09656SatLM: Satisfiability-Aided Language Models Using Declarative Prompting
Xi Ye, Qiaochu Chen, Isil Dillig et al. · Language models & NLP
Prior work has combined chain-of-thought prompting in large language models (LLMs) with programmatic representations to perform effective and transparent reasoning.
- 2023-05arXiv 2305.06983Active Retrieval Augmented Generation
Zhengbao Jiang, Frank F. Xu, Luyu Gao et al. · Language models & NLP
Despite the remarkable ability of large language models (LMs) to comprehend and generate language, they have a tendency to hallucinate and create factually inaccurate output.
- 2023-05arXiv 2305.05862Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text Analytics? A Study on Several Typical Tasks
Xianzhi Li, Samuel Chan, Xiaodan Zhu et al. · Language models & NLP
The most recent large language models(LLMs) such as ChatGPT and GPT-4 have shown exceptional capabilities of generalist models, achieving state-of-the-art performance on a wide range of NLP tasks with little or no adaptation.
- 2023-05arXiv 2305.05176FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance
Lingjiao Chen, Matei Zaharia, James Zou · Machine learning
There is a rapidly growing number of large language models (LLMs) that users can query for a fee.g. GPT-4, ChatGPT, J1-Jumbo, and find that these models have heterogeneous pricing structures, with fees that can differ by two orders of magnitude.
- 2023-05arXiv 2305.03053ZipIt! Merging Models from Different Tasks without Training
George Stoica, Daniel Bolya, Jakob Bjorner et al. · Vision
Typical deep visual recognition models are capable of performing the one task they were trained on.
- 2023-05arXiv 2305.01507A Parameter-free Adaptive Resonance Theory-based Topological Clustering Algorithm Capable of Continual Learning
Naoki Masuyama, Takanori Takebayashi, Yusuke Nojima et al. · Neuroscience & neural computation
, a vigilance parameter) for a node learning process in Adaptive Resonance Theory (ART)-based algorithms has a significant impact on clustering performance.
- 2023-04arXiv 2304.14997Towards Automated Circuit Discovery for Mechanistic Interpretability
Arthur Conmy, Augustine N. Mavor-Parker, Aengus Lynch et al. · Machine learning
Through considerable effort and intuition, several recent works have reverse-engineered nontrivial behaviors of transformer models. This paper systematizes the mechanistic interpretability process they followed.
- 2023-04arXiv 2304.13734The Internal State of an LLM Knows When It's Lying
Amos Azaria, Tom Mitchell · Language models & NLP
While Large Language Models (LLMs) have shown exceptional performance in various tasks, one of their most prominent drawbacks is generating inaccurate or false information with a confident tone.
- 2023-04arXiv 2304.11949Geometric Relational Embeddings: A Survey
Bo Xiong, Mojtaba Nayyeri, Ming Jin et al. · AI & agents
Geometric relational embeddings map relational data as geometric objects that combine vector information suitable for machine learning and structured/relational information for structured/relational reasoning, typically in low dimensions.
- 2023-04arXiv 2304.08244API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs
Minghao Li, Yingxiu Zhao, Bowen Yu et al. · Language models & NLP
Recent research has demonstrated that Large Language Models (LLMs) can enhance their capabilities by utilizing external tools. However, three pivotal questions remain unanswered: (1) How effective are current LLMs in utilizing tools?
Cited inStudy: Genesys program prior art - 2023-04arXiv 2304.03442Generative Agents: Interactive Simulacra of Human Behavior
Joon Sung Park, Joseph C. O'Brien, Carrie J. Cai et al. · Society, software & economics
Believable proxies of human behavior can empower interactive applications ranging from immersive environments to rehearsal spaces for interpersonal communication to prototyping tools.
- 2023-03arXiv 2303.17760CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society
Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani et al. · AI & agents
The rapid advancement of chat-based language models has led to remarkable progress in complex task-solving. However, their success heavily relies on human input to guide the conversation, which can be challenging and time-consuming.
- 2023-03arXiv 2303.17564BloombergGPT: A Large Language Model for Finance
Shijie Wu, Ozan Irsoy, Steven Lu et al. · Machine learning
The use of NLP in the realm of financial technology is broad and complex, with applications ranging from sentiment analysis and named entity recognition to question answering.
- 2023-03arXiv 2303.17580HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face
Yongliang Shen, Kaitao Song, Xu Tan et al. · Language models & NLP
Solving complicated AI tasks with different domains and modalities is a key step toward artificial general intelligence. While there are numerous AI models available for various domains and modalities, they cannot handle complicated AI tasks autonomously.
- 2023-03arXiv 2303.17651Self-Refine: Iterative Refinement with Self-Feedback
Aman Madaan, Niket Tandon, Prakhar Gupta et al. · Language models & NLP
Like humans, large language models (LLMs) do not always generate the best output on their first try. Motivated by how humans refine their written text, we introduce Self-Refine, an approach for improving initial outputs from LLMs through iterative feedback and refinement.
- 2023-03arXiv 2303.16200Natural Selection Favors AIs over Humans
Dan Hendrycks · Society, software & economics
For billions of years, evolution has been the driving force behind the development of life, including humans. Evolution endowed humans with high intelligence, which allowed us to become one of the most successful species on the planet.
- 2023-03arXiv 2303.14177Scaling Expert Language Models with Unsupervised Domain Discovery
Suchin Gururangan, Margaret Li, Mike Lewis et al. · Language models & NLP
Large language models are typically trained densely: all parameters are updated with respect to all inputs. This requires synchronization of billions of parameters across thousands of GPUs.
- 2023-03arXiv 2303.14186TRAK: Attributing Model Behavior at Scale
Sung Min Park, Kristian Georgiev, Andrew Ilyas et al. · Machine learning
The goal of data attribution is to trace model predictions back to training data. Despite a long line of work towards this goal, existing approaches to data attribution tend to force users to choose between computational tractability and efficacy.
- 2023-03arXiv 2303.10845PanGu-Σ: Towards Trillion Parameter Language Model with Sparse Heterogeneous Computing
Xiaozhe Ren, Pingyi Zhou, Xinfan Meng et al. · Language models & NLP
The scaling of large language models has greatly improved natural language understanding, generation, and reasoning.085T parameters named PanGu-Σ.
- 2023-03arXiv 2303.11366Reflexion: Language Agents with Verbal Reinforcement Learning
Noah Shinn, Federico Cassano, Edward Berman et al. · AI & agents
, games, compilers, APIs) as goal-driven agents. However, it remains challenging for these language agents to quickly and efficiently learn from trial-and-error as traditional reinforcement learning methods require extensive training samples and expensive model fine-tuning.
- 2023-03arXiv 2303.10725SIESTA: Efficient Online Continual Learning with Sleep
Md Yousuf Harun, Jhair Gallardo, Tyler L. Hayes et al. · Vision
In supervised continual learning, a deep neural network (DNN) is updated with an ever-growing data stream. Unlike the offline setting where data is shuffled, we cannot make any distributional assumptions about the data stream.
Cited inStudy: Genesys program prior art - 2023-03arXiv 2303.08774GPT-4 Technical Report
OpenAI, Josh Achiam, Steven Adler et al. · Language models & NLP
We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs.
- 2023-03arXiv 2303.08896SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models
Potsawee Manakul, Adian Liusie, Mark J. F. Gales · Language models & NLP
Generative Large Language Models (LLMs) such as GPT-3 are capable of generating highly fluent responses to a wide variety of user prompts. However, LLMs are known to hallucinate facts and make non-factual statements which can undermine trust in their output.
- 2023-03arXiv 2303.08112Eliciting Latent Predictions from Transformers with the Tuned Lens
Nora Belrose, Igor Ostrovsky, Lev McKinney et al. · Machine learning
We analyze transformers from the perspective of iterative inference, seeking to understand how model predictions are refined layer by layer.
- 2023-03arXiv 2303.07338Revisiting Class-Incremental Learning with Pre-Trained Models: Generalizability and Adaptivity are All You Need
Da-Wei Zhou, Zi-Wen Cai, Han-Jia Ye et al. · Machine learning
Class-incremental learning (CIL) aims to adapt to emerging new classes without forgetting old ones. Traditional CIL models are trained from scratch to continually acquire knowledge as data evolves.
Cited inStudy: Genesys program prior art - 2023-03arXiv 2303.06572Continual Visual Reinforcement Learning with A Life-Long World Model
Minting Pan, Wendong Zhang, Geng Chen et al. · Machine learning
Learning physical dynamics in a series of non-stationary environments is a challenging but essential task for model-based reinforcement learning (MBRL) with visual inputs.
- 2023-03arXiv 2303.02966Non-Parametric Outlier Synthesis
Leitian Tao, Xuefeng Du, Xiaojin Zhu et al. · Machine learning
Out-of-distribution (OOD) detection is indispensable for safely deploying machine learning models in the wild. One of the key challenges is that models lack supervision signals from unknown data, and as a result, can produce overconfident predictions on OOD data.
- 2023-02arXiv 2302.12902The Dormant Neuron Phenomenon in Deep Reinforcement Learning
Ghada Sokar, Rishabh Agarwal, Pablo Samuel Castro et al. · Machine learning
In this work we identify the dormant neuron phenomenon in deep reinforcement learning, where an agent's network suffers from an increasing number of inactive neurons, thereby affecting network expressivity.
- 2023-02arXiv 2302.11014An Updated Assessment of Reinforcement Learning for Macro Placement
Chung-Kuan Cheng, Andrew B. Kahng, Sayak Kundu et al. · Machine learning
We provide an improved assessment of Google Brain's deep reinforcement learning approach to macro placement and its updated Circuit Training (CT) implementation in GitHub.
Cited inStudy: Superintelligence - 2023-02arXiv 2302.10108Anytime-Valid Confidence Sequences in an Enterprise A/B Testing Platform
Akash V. Maharaj, Ritwik Sinha, David Arbour et al. · Companion Proceedings of the ACM Web Conference 2023 (WWW '23 Companion) · Statistics & theory
A/B tests are the gold standard for evaluating digital experiences on the web. However, traditional "fixed-horizon" statistical methods are often incompatible with the needs of modern industry practitioners as they do not permit continuous monitoring of experiments.
Cited inInternal notes - 2023-02arXiv 2302.06600Task-Specific Skill Localization in Fine-tuned Language Models
Abhishek Panigrahi, Nikunj Saunshi, Haoyu Zhao et al. · Language models & NLP
Pre-trained language models can be fine-tuned to solve diverse NLP tasks, including in few-shot settings. Thus fine-tuning allows the model to quickly pick up task-specific ``skills,'' but there has been limited study of where these newly-learnt skills reside inside the massive model.
- 2023-02arXiv 2302.03648Class-Incremental Learning: A Survey
Da-Wei Zhou, Qi-Wei Wang, Zhi-Hong Qi et al. · Vision
, CNNs and Vision Transformers, have achieved impressive achievements in many vision tasks in the closed world. However, novel classes emerge from time to time in our ever-changing world, requiring a learning system to acquire new knowledge continually.
- 2023-02arXiv 2302.01386Continual Learning with Scaled Gradient Projection
Gobinda Saha, Kaushik Roy · Proceedings of Thirty-Seventh AAAI Conference on Artificial Intelligence (2023) · Machine learning
In neural networks, continual learning results in gradient interference among sequential tasks, leading to catastrophic forgetting of old tasks while learning new ones.
- 2023-01arXiv 2301.13379Faithful Chain-of-Thought Reasoning
Qing Lyu, Shreya Havaldar, Adam Stein et al. · Language models & NLP
faithfulness). We propose Faithful CoT, a reasoning framework involving two stages: Translation (Natural Language query symbolic reasoning chain) and Problem Solving (reasoning chain answer), using an LM and a deterministic solver respectively.
- 2023-01arXiv 2302.00487A Comprehensive Survey of Continual Learning: Theory, Method and Application
Liyuan Wang, Xingxing Zhang, Hang Su et al. · Machine learning
To cope with real-world dynamics, an intelligent system needs to incrementally acquire, update, accumulate, and exploit knowledge throughout its lifetime. This ability, known as continual learning, provides a foundation for AI systems to develop themselves adaptively.
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.
