Fetching the paper…
Reading the bibliography…
Understanding the mechanisms behind Large Language Models (LLMs) is crucial for designing improved models and strategies.
Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
Earlier work this paper cites.
Language models are few-shot learners
Tom B Brown · 2020
Earlier work this paper cites.
Realtoxicityprompts: Evaluating neural toxic degeneration in language models
Samuel Gehman, Suchin Gururangan, Maarten Sap, Yejin Choi, and Noah A Smith · 2020
Earlier work this paper cites.
Investigating gender bias in language models using causal mediation analysis
Jesse Vig, Sebastian Gehrmann, Yonatan Belinkov, Sharon Qian, Daniel Nevo, Yaron Singer, and Stuart Shieber · 2020
Earlier work this paper cites.
Knowledge neurons in pretrained transformers
Damai Dai, Li Dong, Yaru Hao, Zhifang Sui, Baobao Chang, and Furu Wei · 2021
Earlier work this paper cites.
A mathematical framework for transformer circuits
Nelson Elhage, Neel Nanda, Catherine Olsson, Tom Henighan, Nicholas Joseph, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, Tom Conerly, et al · 2021
Earlier work this paper cites.
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
Earlier work this paper cites.
Analyzing transformers in embedding space
Guy Dar, Mor Geva, Ankit Gupta, and Jonathan Berant · 2022
Earlier work this paper cites.
Transformer feed-forward layers build predictions by promoting concepts in the vocabulary space
Mor Geva, Avi Caciularu, Kevin Ro Wang, and Yoav Goldberg · 2022
Earlier work this paper cites.
Locating and editing factual associations in gpt
Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov · 2022
Earlier work this paper cites.
In-context learning and induction heads
Catherine Olsson, Nelson Elhage, Neel Nanda, Nicholas Joseph, Nova DasSarma, Tom Henighan, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, et al · 2022
Earlier work this paper cites.
Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
Earlier work this paper cites.
Neuron-level interpretation of deep nlp models: A survey
Hassan Sajjad, Nadir Durrani, and Fahim Dalvi · 2022
Earlier work this paper cites.
Interpretability in the wild: a circuit for indirect object identification in gpt-2 small
Kevin Wang, Alexandre Variengien, Arthur Conmy, Buck Shlegeris, and Jacob Steinhardt · 2022
Earlier work this paper cites.
Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality
Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E Gonzalez, et al · 2023
Earlier work this paper cites.
Towards automated circuit discovery for mechanistic interpretability
Arthur Conmy, Augustine Mavor-Parker, Aengus Lynch, Stefan Heimersheim, and Adrià Garriga-Alonso · 2023
Earlier work this paper cites.
Llms to the moon? reddit market sentiment analysis with large language models
Xiang Deng, Vasilisa Bashlovkina, Feng Han, Simon Baumgartner, and Michael Bendersky · 2023
Cited alongside, same era.
Interpreting clip’s image representation via text-based decomposition
Yossi Gandelsman, Alexei A Efros, and Jacob Steinhardt · 2023
Cited alongside, same era.
Dissecting recall of factual associations in auto-regressive language models
Mor Geva, Jasmijn Bastings, Katja Filippova, and Amir Globerson · 2023
Cited alongside, same era.
Successor heads: Recurring, interpretable attention heads in the wild
Rhys Gould, Euan Ong, George Ogden, and Arthur Conmy · 2023
Cited alongside, same era.
Finding neurons in a haystack: Case studies with sparse probing
Evaluating reading comprehension exercises generated by llms: A showcase of chatgpt in education applications
Changrong Xiao, Sean Xin Xu, Kunpeng Zhang, Yufang Wang, and Lei Xia · 2023
Later among the works it cites.
Llm lies: Hallucinations are not bugs, but features as adversarial examples
Jia-Yu Yao, Kun-Peng Ning, Zhen-Hui Liu, Mu-Nan Ning, and Li Yuan · 2023
Later among the works it cites.
Towards best practices of activation patching in language models: Metrics and methods
Fred Zhang and Neel Nanda · 2023
Later among the works it cites.
Analyzing and mitigating object hallucination in large vision-language models
Yiyang Zhou, Chenhang Cui, Jaehong Yoon, Linjun Zhang, Zhun Deng, Chelsea Finn, Mohit Bansal, and Huaxiu Yao · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Wes Gurnee, Neel Nanda, Matthew Pauly, Katherine Harvey, Dmitrii Troitskii, and Dimitris Bertsimas · 2023
Cited alongside, same era.
Gender bias and stereotypes in large language models
Hadas Kotek, Rikker Dockum, and David Sun · 2023
Cited alongside, same era.
Evaluating object hallucination in large vision-language models
Yifan Li, Yifan Du, Kun Zhou, Jinpeng Wang, Wayne Xin Zhao, and Ji-Rong Wen · 2023
Cited alongside, same era.
Tom Lieberum, Matthew Rahtz, János Kramár, Neel Nanda, Geoffrey Irving, Rohin Shah, and Vladimir Mikulik · 2023
Cited alongside, same era.
Is this the subspace you are looking for? an interpretability illusion for subspace activation patching
Aleksandar Makelov, Georg Lange, Atticus Geiger, and Neel Nanda · 2023
Cited alongside, same era.
Circuit component reuse across tasks in transformer language models
Jack Merullo, Carsten Eickhoff, and Ellie Pavlick · 2023
Cited alongside, same era.
Fact finding: Attempting to reverse-engineer factual recall on the neuron level, 2023
Neel Nanda, Senthooran Rajamanoharan, János Kramár, and Rohin Shah · 2023
Cited alongside, same era.
Alessandro Stolfo, Yonatan Belinkov, and Mrinmaya Sachan · 2023
Cited alongside, same era.
Zechen Bai, Pichao Wang, Tianjun Xiao, Tong He, Zongbo Han, Zheng Zhang, and Mike Zheng Shou · 2024
Closest in time.
Understanding information storage and transfer in multi-modal large language models
Samyadeep Basu, Martin Grayson, Cecily Morrison, Besmira Nushi, Soheil Feizi, and Daniela Massiceti · 2024
Closest in time.
How does gpt-2 compute greater-than?: Interpreting mathematical abilities in a pre-trained language model
Michael Hanna, Ollie Liu, and Alexandre Variengien · 2024
Closest in time.
Visual hallucinations of multi-modal large language models
Wen Huang, Hongbin Liu, Minxin Guo, and Neil Zhenqiang Gong · 2024
Closest in time.
Fine-tuning enhances existing mechanisms: A case study on entity tracking
Nikhil Prakash, Tamar Rott Shaham, Tal Haklay, Yonatan Belinkov, and David Bau · 2024
Closest in time.
Causal interpretation of self-attention in pre-trained transformers
Raanan Y Rohekar, Yaniv Gurwicz, and Shami Nisimov · 2024
Closest in time.
Lvlm-intrepret: An interpretability tool for large vision-language models
Gabriela Ben Melech Stan, Raanan Yehezkel Rohekar, Yaniv Gurwicz, Matthew Lyle Olson, Anahita Bhiwandiwalla, Estelle Aflalo, Chenfei Wu, Nan Duan, Shao-Yen Tseng, and Vasudev Lal · 2024
Closest in time.
Eyes wide shut? exploring the visual shortcomings of multimodal llms
Shengbang Tong, Zhuang Liu, Yuexiang Zhai, Yi Ma, Yann LeCun, and Saining Xie · 2024
Closest in time.
Interpreting arithmetic mechanism in large language models through comparative neuron analysis
Zeping Yu and Sophia Ananiadou · 2024
Closest in time.
Neuron-level knowledge attribution in large language models
Zeping Yu and Sophia Ananiadou · 2024
Closest in time.
Mm-llms: Recent advances in multimodal large language models
Duzhen Zhang, Yahan Yu, Chenxing Li, Jiahua Dong, Dan Su, Chenhui Chu, and Dong Yu · 2024
Closest in time.