Built on

Language models are few-shot learners
Original
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 1901
Earlier work this paper cites.
Severe sepsis attributable to community-associated methicillin-resistant staphylococcus aureus: an emerging fatal problem
Eric T Castaldo and Edmund Y Yang. 2007 · 2007
Earlier work this paper cites.
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 · 2014
Earlier work this paper cites.
Dense-captioning events in videos
Ranjay Krishna, Kenji Hata, Frederic Ren, Li Fei-Fei, and Juan Carlos Niebles. 2017 · 2017
Earlier work this paper cites.
Object hallucination in image captioning
Original
Anna Rohrbach, Lisa Anne Hendricks, Kaylee Burns, Trevor Darrell, and Kate Saenko. 2018 · 2018
Earlier work this paper cites.
On the opportunities and risks of foundation models
Original
Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al. 2021 · 2021
Earlier work this paper cites.
Plausible may not be faithful: Probing object hallucination in vision-language pre-training
Original
Wenliang Dai, Zihan Liu, Ziwei Ji, Dan Su, and Pascale Fung. 2022 · 2022
Earlier work this paper cites.
High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2022 · 2022
Earlier work this paper cites.
The internal state of an llm knows when its lying
Original
Amos Azaria and Tom Mitchell. 2023 · 2023
Earlier work this paper cites.
Purr: Efficiently editing language model hallucinations by denoising language model corruptions
Original
Anthony Chen, Panupong Pasupat, Sameer Singh, Hongrae Lee, and Kelvin Guu. 2023 · 2023
Earlier work this paper cites.
Chatlaw: Open-source legal large language model with integrated external knowledge bases
Original
Jiaxi Cui, Zongjian Li, Yang Yan, Bohua Chen, and Li Yuan. 2023 · 2023
Earlier work this paper cites.

Similar

Lp-musiccaps: Llm-based pseudo music captioning
Original
SeungHeon Doh, Keunwoo Choi, Jongpil Lee, and Juhan Nam. 2023 · 2023
Cited alongside, same era.
Halo: Estimation and reduction of hallucinations in open-source weak large language models
Original
Mohamed Elaraby, Mengyin Lu, Jacob Dunn, Xueying Zhang, Yu Wang, and Shizhu Liu. 2023 · 2023
Cited alongside, same era.
Rarr: Researching and revising what language models say, using language models
Luyu Gao, Zhuyun Dai, Panupong Pasupat, Anthony Chen, Arun Tejasvi Chaganty, Yicheng Fan, Vincent Zhao, Ni Lao, Hongrae Lee, Da-Cheng Juan, et al. 2023 · 2023
Cited alongside, same era.
Detecting and preventing hallucinations in large vision language models
Original
Anisha Gunjal, Jihan Yin, and Erhan Bas. 2023 · 2023
Cited alongside, same era.
Let’s think frame by frame: Evaluating video chain of thought with video infilling and prediction

Then

Selfcheckgpt: Zero-resource black-box hallucination detection for generative large language models
Original
Potsawee Manakul, Adian Liusie, and Mark J. F. Gales. 2023 · 2023
Closest in time.
Sources of hallucination by large language models on inference tasks
Original
Nick McKenna, Tianyi Li, Liang Cheng, Mohammad Javad Hosseini, Mark Johnson, and Mark Steedman. 2023 · 2023
Closest in time.
Self-contradictory hallucinations of large language models: Evaluation, detection and mitigation
Original
Niels Mündler, Jingxuan He, Slobodan Jenko, and Martin Vechev. 2023 · 2023
Closest in time.
Check your facts and try again: Improving large language models with external knowledge and automated feedback
Original
Baolin Peng, Michel Galley, Pengcheng He, Hao Cheng, Yujia Xie, Yu Hu, Qiuyuan Huang, Lars Liden, Zhou Yu, Weizhu Chen, et al. 2023 · 2023
Closest in time.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Reading the bibliography…

2023

A Survey of Hallucination in Large Foundation Models

Rawte, Vipula, Sheth, Amit, Das, Amitava

Understand

Hallucination in a foundation model (FM) refers to the generation of content that strays from factual reality or includes fabricated information.

This survey paper provides an extensive overview of recent efforts that aim to identify, elucidate, and tackle the problem of hallucination, with a particular focus on ``Large'' Foundation Models (LFMs).
The paper classifies various types of hallucination phenomena that are specific to LFMs and establishes evaluation criteria for assessing the extent of hallucination.
It also examines existing strategies for mitigating hallucination in LFMs and discusses potential directions for future research in this area.

Built on

Language models are few-shot learners
Original
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 1901
Earlier work this paper cites.
Severe sepsis attributable to community-associated methicillin-resistant staphylococcus aureus: an emerging fatal problem
Eric T Castaldo and Edmund Y Yang. 2007 · 2007
Earlier work this paper cites.
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 · 2014
Earlier work this paper cites.
Dense-captioning events in videos
Ranjay Krishna, Kenji Hata, Frederic Ren, Li Fei-Fei, and Juan Carlos Niebles. 2017 · 2017
Earlier work this paper cites.
Object hallucination in image captioning
Original
Anna Rohrbach, Lisa Anne Hendricks, Kaylee Burns, Trevor Darrell, and Kate Saenko. 2018 · 2018
Earlier work this paper cites.
On the opportunities and risks of foundation models
Original
Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al. 2021 · 2021
Earlier work this paper cites.
Plausible may not be faithful: Probing object hallucination in vision-language pre-training
Original
Wenliang Dai, Zihan Liu, Ziwei Ji, Dan Su, and Pascale Fung. 2022 · 2022
Earlier work this paper cites.
High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2022 · 2022
Earlier work this paper cites.
The internal state of an llm knows when its lying
Original
Amos Azaria and Tom Mitchell. 2023 · 2023
Earlier work this paper cites.
Purr: Efficiently editing language model hallucinations by denoising language model corruptions
Original
Anthony Chen, Panupong Pasupat, Sameer Singh, Hongrae Lee, and Kelvin Guu. 2023 · 2023
Earlier work this paper cites.
Chatlaw: Open-source legal large language model with integrated external knowledge bases
Original
Jiaxi Cui, Zongjian Li, Yang Yan, Bohua Chen, and Li Yuan. 2023 · 2023
Earlier work this paper cites.

Similar

Lp-musiccaps: Llm-based pseudo music captioning
Original
SeungHeon Doh, Keunwoo Choi, Jongpil Lee, and Juhan Nam. 2023 · 2023
Cited alongside, same era.
Halo: Estimation and reduction of hallucinations in open-source weak large language models
Original
Mohamed Elaraby, Mengyin Lu, Jacob Dunn, Xueying Zhang, Yu Wang, and Shizhu Liu. 2023 · 2023
Cited alongside, same era.
Rarr: Researching and revising what language models say, using language models
Luyu Gao, Zhuyun Dai, Panupong Pasupat, Anthony Chen, Arun Tejasvi Chaganty, Yicheng Fan, Vincent Zhao, Ni Lao, Hongrae Lee, Da-Cheng Juan, et al. 2023 · 2023
Cited alongside, same era.
Detecting and preventing hallucinations in large vision language models
Original
Anisha Gunjal, Jihan Yin, and Erhan Bas. 2023 · 2023
Cited alongside, same era.
Let’s think frame by frame: Evaluating video chain of thought with video infilling and prediction

Then

Selfcheckgpt: Zero-resource black-box hallucination detection for generative large language models
Original
Potsawee Manakul, Adian Liusie, and Mark J. F. Gales. 2023 · 2023
Closest in time.
Sources of hallucination by large language models on inference tasks
Original
Nick McKenna, Tianyi Li, Liang Cheng, Mohammad Javad Hosseini, Mark Johnson, and Mark Steedman. 2023 · 2023
Closest in time.
Self-contradictory hallucinations of large language models: Evaluation, detection and mitigation
Original
Niels Mündler, Jingxuan He, Slobodan Jenko, and Martin Vechev. 2023 · 2023
Closest in time.
Check your facts and try again: Improving large language models with external knowledge and automated feedback
Original
Baolin Peng, Michel Galley, Pengcheng He, Hao Cheng, Yujia Xie, Yu Hu, Qiuyuan Huang, Lars Liden, Zhou Yu, Weizhu Chen, et al. 2023 · 2023
Closest in time.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Original

Vaishnavi Himakunthala, Andy Ouyang, Daniel Rose, Ryan He, Alex Mei, Yujie Lu, Chinmay Sonar, Michael Saxon, and William Yang Wang. 2023 · 2023

Cited alongside, same era.

Citation: A key to building responsible and accountable large language models

Original

Jie Huang and Kevin Chen-Chuan Chang. 2023 · 2023

Cited alongside, same era.

Dehallucinating large language models using formal methods guided iterative prompting

Susmit Jha, Sumit Kumar Jha, Patrick Lincoln, Nathaniel D Bastian, Alvaro Velasquez, and Sandeep Neema. 2023 · 2023

Cited alongside, same era.

Survey of hallucination in natural language generation

Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung. 2023 · 2023

Cited alongside, same era.

Putting people in their place: Affordance-aware human insertion into scenes

Sumith Kulal, Tim Brooks, Alex Aiken, Jiajun Wu, Jimei Yang, Jingwan Lu, Alexei A Efros, and Krishna Kumar Singh. 2023 · 2023

Cited alongside, same era.

Models see hallucinations: Evaluating the factuality in video captioning

Original

Hui Liu and Xiaojun Wan. 2023 · 2023

Cited alongside, same era.

Zero-resource hallucination prevention for large language models

Original

Junyu Luo, Cao Xiao, and Fenglong Ma. 2023 · 2023

Cited alongside, same era.

Audio-journey: Efficient visual+ llm-aided audio encodec diffusion

Juncheng B Li, Jackson Sam Michaels, Laura Yao, Lijun Yu, Zach Wood-Doughty, and Florian Metze. 2023a

Cited in the paper.

mmt5: Modular multilingual pre-training solves source language hallucinations

Original

Jonas Pfeiffer, Francesco Piccinno, Massimo Nicosia, Xinyi Wang, Machel Reid, and Sebastian Ruder. 2023 · 2023

Closest in time.

Med-halt: Medical domain hallucination test for large language models

Original

Logesh Kumar Umapathi, Ankit Pal, and Malaikannan Sankarasubbu. 2023 · 2023

Closest in time.

A stitch in time saves nine: Detecting and mitigating hallucinations of llms by validating low-confidence generation

Original

Neeraj Varshney, Wenlin Yao, Hongming Zhang, Jianshu Chen, and Dong Yu. 2023 · 2023

Closest in time.

Are ai models doomed to always hallucinate?

Kyle Wiggers. 2023 · 2023

Closest in time.

Hallucination improves the performance of unsupervised visual representation learning

Original

Jing Wu, Jennifer Hobbs, and Naira Hovakimyan. 2023 · 2023

Closest in time.

Deficiency-aware masked transformer for video inpainting

Original

Yongsheng Yu, Heng Fan, and Libo Zhang. 2023 · 2023

Closest in time.