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Dan Friedman
Dan Friedman
在 princeton.edu 的电子邮件经过验证 - 首页
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引用次数
引用次数
年份
Factual probing is [mask]: Learning vs. learning to recall
Z Zhong, D Friedman, D Chen
arXiv preprint arXiv:2104.05240, 2021
3982021
Scisummnet: A large annotated corpus and content-impact models for scientific paper summarization with citation networks
M Yasunaga, J Kasai, R Zhang, AR Fabbri, I Li, D Friedman, DR Radev
Proceedings of the AAAI conference on artificial intelligence 33 (01), 7386-7393, 2019
2332019
Embers of autoregression: Understanding large language models through the problem they are trained to solve
RT McCoy, S Yao, D Friedman, M Hardy, TL Griffiths
arXiv preprint arXiv:2309.13638, 2023
1122023
The vendi score: A diversity evaluation metric for machine learning
D Friedman, AB Dieng
arXiv preprint arXiv:2210.02410, 2022
782022
Syntax-aware neural semantic role labeling with supertags
J Kasai, D Friedman, R Frank, D Radev, O Rambow
arXiv preprint arXiv:1903.05260, 2019
432019
Learning transformer programs
D Friedman, A Wettig, D Chen
Advances in Neural Information Processing Systems 36, 2024
352024
Measuring inductive biases of in-context learning with underspecified demonstrations
C Si, D Friedman, N Joshi, S Feng, D Chen, H He
arXiv preprint arXiv:2305.13299, 2023
312023
Single-dataset experts for multi-dataset question answering
D Friedman, B Dodge, D Chen
arXiv preprint arXiv:2109.13880, 2021
262021
Finding dataset shortcuts with grammar induction
D Friedman, A Wettig, D Chen
arXiv preprint arXiv:2210.11560, 2022
112022
Embers of autoregression show how large language models are shaped by the problem they are trained to solve
RT McCoy, S Yao, D Friedman, MD Hardy, TL Griffiths
Proceedings of the National Academy of Sciences 121 (41), e2322420121, 2024
102024
Interpretability illusions in the generalization of simplified models
D Friedman, A Lampinen, L Dixon, D Chen, A Ghandeharioun
arXiv preprint arXiv:2312.03656, 2023
82023
The Heuristic Core: Understanding Subnetwork Generalization in Pretrained Language Models
A Bhaskar, D Friedman, D Chen
arXiv preprint arXiv:2403.03942, 2024
72024
Linguistically rich vector representations of supertags for TAG parsing
D Friedman, J Kasai, RT McCoy, R Frank, F Davis, O Rambow
Proceedings of the 13th International Workshop on Tree Adjoining Grammars …, 2017
42017
What Spurious Features Can Pretrained Language Models Combat?
C Si, D Friedman, N Joshi, S Feng, D Chen, H He
32023
Finding transformer circuits with edge pruning
A Bhaskar, A Wettig, D Friedman, D Chen
arXiv preprint arXiv:2406.16778, 2024
22024
Comparing Representational and Functional Similarity in Small Transformer Language Models
D Friedman, AK Lampinen, L Dixon, D Chen, A Ghandeharioun
UniReps: the First Workshop on Unifying Representations in Neural Models, 2023
22023
When a language model is optimized for reasoning, does it still show embers of autoregression? An analysis of OpenAI o1
RT McCoy, S Yao, D Friedman, MD Hardy, TL Griffiths
arXiv preprint arXiv:2410.01792, 2024
12024
Representing rule-based chatbots with transformers
D Friedman, A Panigrahi, D Chen
arXiv preprint arXiv:2407.10949, 2024
12024
Continual Memorization of Factoids in Large Language Models
H Chen, J Geng, A Bhaskar, D Friedman, D Chen
arXiv preprint arXiv:2411.07175, 2024
2024
A Neural Network Approach to Value-at-Risk Forecasting
D Friedman, A Matell
2024
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