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.
- 2023-11arXiv 2311.08718Decomposing Uncertainty for Large Language Models through Input Clarification Ensembling
Bairu Hou, Yujian Liu, Kaizhi Qian et al. · Language models & NLP
Uncertainty decomposition refers to the task of decomposing the total uncertainty of a predictive model into aleatoric (data) uncertainty, resulting from inherent randomness in the data-generating process, and epistemic (model) uncertainty, resulting from missing information in the model's training data.
- 2023-11arXiv 2311.08401Fine-tuning Language Models for Factuality
Katherine Tian, Eric Mitchell, Huaxiu Yao et al. · Language models & NLP
The fluency and creativity of large pre-trained language models (LLMs) have led to their widespread use, sometimes even as a replacement for traditional search engines.
- 2023-11arXiv 2311.08516LLMs cannot find reasoning errors, but can correct them given the error location
Gladys Tyen, Hassan Mansoor, Victor Cărbune et al. · AI & agents
g., 2023). In this paper, we show that poor self-correction performance stems from LLMs' inability to find logical mistakes, rather than their ability to correct a known mistake.
- 2023-11arXiv 2311.08596Are You Sure? Challenging LLMs Leads to Performance Drops in The FlipFlop Experiment
Philippe Laban, Lidiya Murakhovs'ka, Caiming Xiong et al. · Language models & NLP
The interactive nature of Large Language Models (LLMs) theoretically allows models to refine and improve their answers, yet systematic analysis of the multi-turn behavior of LLMs remains limited.
- 2023-11arXiv 2311.07383LM-Polygraph: Uncertainty Estimation for Language Models
Ekaterina Fadeeva, Roman Vashurin, Akim Tsvigun et al. · Language models & NLP
Recent advancements in the capabilities of large language models (LLMs) have paved the way for a myriad of groundbreaking applications in various fields., fabricate facts without providing users an apparent means to discern the veracity of their statements.
- 2023-10arXiv 2310.13548Towards Understanding Sycophancy in Language Models
Mrinank Sharma, Meg Tong, Tomasz Korbak et al. · Language models & NLP
Human feedback is commonly utilized to finetune AI assistants. But human feedback may also encourage model responses that match user beliefs over truthful ones, a behaviour known as sycophancy.
Cited inCoherence is selected, anchored, then pruned: a literature synthesis on logical coherence in LLM world modelsStable wrong beliefs evade every self-check: a literature synthesis on model self-knowledge and confident errorsStudy: Logical coherence of LLM world modelsStudy: Model self knowledge and confident errors - 2023-10arXiv 2310.12963AutoMix: Automatically Mixing Language Models
Pranjal Aggarwal, Aman Madaan, Ankit Anand et al. · Language models & NLP
Large language models (LLMs) are now available from cloud API providers in various sizes and configurations. While this diversity offers a broad spectrum of choices, effectively leveraging the options to optimize computational cost and performance remains challenging.
- 2023-10arXiv 2310.11511Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
Akari Asai, Zeqiu Wu, Yizhong Wang et al. · Language models & NLP
Despite their remarkable capabilities, large language models (LLMs) often produce responses containing factual inaccuracies due to their sole reliance on the parametric knowledge they encapsulate.
- 2023-10arXiv 2310.05002Self-Knowledge Guided Retrieval Augmentation for Large Language Models
Yile Wang, Peng Li, Maosong Sun et al. · Language models & NLP
Large language models (LLMs) have shown superior performance without task-specific fine-tuning. Despite the success, the knowledge stored in the parameters of LLMs could still be incomplete and difficult to update due to the computational costs.
- 2023-10arXiv 2310.01798Large Language Models Cannot Self-Correct Reasoning Yet
Jie Huang, Xinyun Chen, Swaroop Mishra et al. · Language models & NLP
Large Language Models (LLMs) have emerged as a groundbreaking technology with their unparalleled text generation capabilities across various applications. Nevertheless, concerns persist regarding the accuracy and appropriateness of their generated content.
Cited inCoherence is selected, anchored, then pruned: a literature synthesis on logical coherence in LLM world modelsStable wrong beliefs evade every self-check: a literature synthesis on model self-knowledge and confident errorsStudy: Civilization as a superintelligence modelStudy: Cross generational teaching and examinationStudy: Logical coherence of LLM world modelsStudy: Model self knowledge and confident errorsStudy: Orchestrated specialists and bridge synthesis - 2023-09arXiv 2309.11495Chain-of-Verification Reduces Hallucination in Large Language Models
Shehzaad Dhuliawala, Mojtaba Komeili, Jing Xu et al. · Language models & NLP
Generation of plausible yet incorrect factual information, termed hallucination, is an unsolved issue in large language models. We study the ability of language models to deliberate on the responses they give in order to correct their mistakes.
- 2023-07arXiv 2307.02764When Does Confidence-Based Cascade Deferral Suffice?
Wittawat Jitkrittum, Neha Gupta, Aditya Krishna Menon et al. · Machine learning
Cascades are a classical strategy to enable inference cost to vary adaptively across samples, wherein a sequence of classifiers are invoked in turn. A deferral rule determines whether to invoke the next classifier in the sequence, or to terminate prediction.
- 2023-07arXiv 2307.01928Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners
Allen Z. Ren, Anushri Dixit, Alexandra Bodrova et al. · Robotics & control
Large language models (LLMs) exhibit a wide range of promising capabilities -- from step-by-step planning to commonsense reasoning -- that may provide utility for robots, but remain prone to confidently hallucinated predictions.
- 2023-06arXiv 2306.13063Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs
Miao Xiong, Zhiyuan Hu, Xinyang Lu et al. · Language models & NLP
Empowering large language models to accurately express confidence in their answers is essential for trustworthy decision-making.
- 2023-06arXiv 2306.10193Conformal Language Modeling
Victor Quach, Adam Fisch, Tal Schuster et al. · Language models & NLP
We propose a novel approach to conformal prediction for generative language models (LMs). Standard conformal prediction produces prediction sets -- in place of single predictions -- that have rigorous, statistical performance guarantees.
- 2023-05arXiv 2305.18153Do Large Language Models Know What They Don't Know?
Zhangyue Yin, Qiushi Sun, Qipeng Guo et al. · Language models & NLP
Large language models (LLMs) have a wealth of knowledge that allows them to excel in various Natural Language Processing (NLP) tasks. Current research focuses on enhancing their performance within their existing knowledge.
- 2023-05arXiv 2305.14975Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback
Katherine Tian, Eric Mitchell, Allan Zhou et al. · Language models & NLP
A trustworthy real-world prediction system should produce well-calibrated confidence scores; that is, its confidence in an answer should be indicative of the likelihood that the answer is correct, enabling deferral to an expert in cases of low-confidence predictions.
- 2023-05arXiv 2305.13712Knowledge of Knowledge: Exploring Known-Unknowns Uncertainty with Large Language Models
Alfonso Amayuelas, Kyle Wong, Liangming Pan et al. · Language models & NLP
This paper investigates the capabilities of Large Language Models (LLMs) in the context of understanding their knowledge and uncertainty over questions. Specifically, we focus on addressing known-unknown questions, characterized by high uncertainty due to the absence of definitive answers.
- 2023-05arXiv 2305.12616Conformal Prediction With Conditional Guarantees
Isaac Gibbs, John J. Cherian, Emmanuel J. Candès · Statistics & theory
We consider the problem of constructing distribution-free prediction sets with finite-sample conditional guarantees. Prior work has shown that it is impossible to provide exact conditional coverage universally in finite samples.
- 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 2305.06983Active 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 2305.05176FrugalGPT: 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.
- 2023-03arXiv 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.
- 2022-12arXiv 2212.10511When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories
Alex Mallen, Akari Asai, Victor Zhong et al. · Language models & NLP
Despite their impressive performance on diverse tasks, large language models (LMs) still struggle with tasks requiring rich world knowledge, implying the limitations of relying solely on their parameters to encode a wealth of world knowledge.
- 2022-09arXiv 2209.15558Out-of-Distribution Detection and Selective Generation for Conditional Language Models
Jie Ren, Jiaming Luo, Yao Zhao et al. · Language models & NLP
Machine learning algorithms typically assume independent and identically distributed samples in training and at test time.
- 2022-08arXiv 2208.02814Conformal Risk Control
Anastasios N. Angelopoulos, Stephen Bates, Adam Fisch et al. · Statistics & theory
We extend conformal prediction to control the expected value of any monotone loss function. The algorithm generalizes split conformal prediction together with its coverage guarantee.
- 2022-07arXiv 2207.05221Language Models (Mostly) Know What They Know
Saurav Kadavath, Tom Conerly, Amanda Askell et al. · Language models & NLP
We study whether language models can evaluate the validity of their own claims and predict which questions they will be able to answer correctly. We first show that larger models are well-calibrated on diverse multiple choice and true/false questions when they are provided in the right format.
- 2022-05arXiv 2205.14334Teaching Models to Express Their Uncertainty in Words
Stephanie Lin, Jacob Hilton, Owain Evans · Language models & NLP
We show that a GPT-3 model can learn to express uncertainty about its own answers in natural language -- without use of model logits. "90% confidence" or "high confidence").
- 2021-10arXiv 2110.01052Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control
Anastasios N. Angelopoulos, Stephen Bates, Emmanuel J. Candès et al. · Machine learning
We introduce a framework for calibrating machine learning models so that their predictions satisfy explicit, finite-sample statistical guarantees. Our calibration algorithms work with any underlying model and (unknown) data-generating distribution and do not require model refitting.
- 2021-09arXiv 2109.07958TruthfulQA: Measuring How Models Mimic Human Falsehoods
Stephanie Lin, Jacob Hilton, Owain Evans · Language models & NLP
We propose a benchmark to measure whether a language model is truthful in generating answers to questions. The benchmark comprises 817 questions that span 38 categories, including health, law, finance and politics.
- 2020-06arXiv 2006.09462Selective Question Answering under Domain Shift
Amita Kamath, Robin Jia, Percy Liang · Language models & NLP
To avoid giving wrong answers, question answering (QA) models need to know when to abstain from answering. Moreover, users often ask questions that diverge from the model's training data, making errors more likely and thus abstention more critical.
- 2020-06arXiv 2006.01862Consistent Estimators for Learning to Defer to an Expert
Hussein Mozannar, David Sontag · Machine learning
Learning algorithms are often used in conjunction with expert decision makers in practical scenarios, however this fact is largely ignored when designing these algorithms.
- 2019-04arXiv 1904.06019Conformal Prediction Under Covariate Shift
Ryan J. Tibshirani, Rina Foygel Barber, Emmanuel J. Candes et al. · Statistics & theory
We extend conformal prediction methodology beyond the case of exchangeable data.
- 2019-04arXiv 1904.01685Measuring Calibration in Deep Learning
Jeremy Nixon, Mike Dusenberry, Ghassen Jerfel et al. · Machine learning
Overconfidence and underconfidence in machine learning classifiers is measured by calibration: the degree to which the probabilities predicted for each class match the accuracy of the classifier on that prediction.
- 2019-03arXiv 1903.04684The limits of distribution-free conditional predictive inference
Rina Foygel Barber, Emmanuel J. Candès, Aaditya Ramdas et al. · Statistics & theory
We consider the problem of distribution-free predictive inference, with the goal of producing predictive coverage guarantees that hold conditionally rather than marginally.
- 2018-05arXiv 1805.08206Bias-Reduced Uncertainty Estimation for Deep Neural Classifiers
Yonatan Geifman, Guy Uziel, Ran El-Yaniv · Machine learning
We consider the problem of uncertainty estimation in the context of (non-Bayesian) deep neural classification. In this context, all known methods are based on extracting uncertainty signals from a trained network optimized to solve the classification problem at hand.
- 2017-05arXiv 1705.08500Selective Classification for Deep Neural Networks
Yonatan Geifman, Ran El-Yaniv · Machine learning
Selective classification techniques (also known as reject option) have not yet been considered in the context of deep neural networks (DNNs). These techniques can potentially significantly improve DNNs prediction performance by trading-off coverage.
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.
