---
title: "Factuality Challenges in the Era of Large — Augenstein 2023"
description: "This perspective article, written by researchers and fact-checking practitioners, looks at how chatbots built on LLMs such as ChatGPT, Bing Chat and Bard…"
authors: ["Isabelle Augenstein", "Timothy Baldwin", "Meeyoung Cha", "Tanmoy Chakraborty", "Giovanni Luca Ciampaglia", "David Corney", "Renee DiResta", "Emilio Ferrara", "Scott Hale", "Alon Halevy", "Eduard Hovy", "Heng Ji", "Filippo Menczer", "Ruben Miguez", "Preslav Nakov", "Dietram Scheufele", "Shivam Sharma", "Giovanni Zagni"]
year: 2023
venue: "arXiv:2310.05189 · Nat Mach Intell 6, 852--863 (2024)"
source: "https://arxiv.org/abs/2310.05189"
doi: "10.1038/s42256-024-00881-z"
arxiv: "2310.05189"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/augenstein-2023-factuality-challenges-era-large-language/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/augenstein-2023-factuality-challenges-era-large-language/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2023](https://md.research.elamur.ai/free/en/papers/2023/page/3/) / Factuality Challenges in the Era of Large Language Models

# Factuality Challenges in the Era of Large Language Models

**Authors:** [Isabelle Augenstein](https://md.research.elamur.ai/free/en/authors/isabelle-augenstein/), [Timothy Baldwin](https://md.research.elamur.ai/free/en/authors/timothy-baldwin/), Meeyoung Cha, [Tanmoy Chakraborty](https://md.research.elamur.ai/free/en/authors/tanmoy-chakraborty/), Giovanni Luca Ciampaglia, [David Corney](https://md.research.elamur.ai/free/en/authors/david-corney/), Renee DiResta, Emilio Ferrara, [Scott Hale](https://md.research.elamur.ai/free/en/authors/scott-a-hale/), Alon Halevy, [Eduard Hovy](https://md.research.elamur.ai/free/en/authors/eduard-hovy/), [Heng Ji](https://md.research.elamur.ai/free/en/authors/heng-ji/) et al. (18 authors) · **Institutions:** [Stanford University](https://md.research.elamur.ai/free/en/universities/stanford-university/) · [Meta](https://md.research.elamur.ai/free/en/companies/meta/) · [University of Oxford](https://md.research.elamur.ai/free/en/universities/university-of-oxford/) · [Carnegie Mellon University](https://md.research.elamur.ai/free/en/universities/carnegie-mellon-university/) · [University of Illinois Urbana-Champaign](https://md.research.elamur.ai/free/en/universities/university-of-illinois-urbana-champaign/) · [University of Wisconsin–Madison](https://md.research.elamur.ai/free/en/universities/university-of-wisconsin-madison/) · [University of Southern California](https://md.research.elamur.ai/free/en/universities/university-of-southern-california/) · [University of Maryland, College Park](https://md.research.elamur.ai/free/en/universities/university-of-maryland-college-park/) and 7 more · **Year:** 2023 · **Venue:** arXiv:2310.05189 · Nat Mach Intell 6, 852--863 (2024) · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://doi.org/10.1038/s42256-024-00881-z) · [PDF](https://arxiv.org/pdf/2310.05189) · [DOI 10.1038/s42256-024-00881-z](https://doi.org/10.1038/s42256-024-00881-z) · [arXiv 2310.05189](https://arxiv.org/abs/2310.05189) · [OpenAlex](https://openalex.org/W4401815218)

**Citations:** 171 citations (OpenAlex, 2026-10-14) · [70](https://md.research.elamur.ai/free/en/papers/augenstein-2023-factuality-challenges-era-large-language/cited-by/) in our library, of the 140884 papers whose bibliographies we read

## Summary
This perspective article, written by researchers and fact-checking practitioners, looks at how chatbots built on LLMs such as ChatGPT, Bing Chat and Bard affect the reliability of public information. It presents no new dataset or model. Instead, it surveys existing evidence and frames the problem in two parts:
- **Unintended errors:** well-meant systems can hallucinate or produce poorly sourced answers, delivered in a confident tone, on sensitive topics like health and finance.
- **Deliberate misuse:** malicious actors can use LLMs for scams, fake news sites and bot networks at scale.

The authors also note a feedback loop: disinformation produced with GenAI can end up in future training data. They consider where LLMs might help fact-checkers, such as summarization, stance detection and chat over curated corpora, and point out the limits of each. They close with an agenda organized around four areas: international coordination, regulation, AI literacy and technical safeguards. The paper matters because fact-checkers, newsrooms and policymakers can use it as a structured map of risks and countermeasures.

## Key points
- Evidence on undersourcing: in a cited study of generative search engines, close to 50% of generated sentences had no citation. Of the sentences that did cite something, only about 75% were actually backed by their source.
- An analysis of 6,594 real chatbot conversations during the pandemic found that 30% of users searched with the keyword "COVID-19". The authors see this as risky because chatbots cope poorly with emerging or contested topics.
- Fact-checks written by ChatGPT, even accurate ones, did not help people judge headline accuracy, and in some cases they strengthened belief in false claims.
- Real incidents are cited, including Bard's incorrect claim about a James Webb telescope exoplanet image and Replika spreading a Bill Gates COVID-19 conspiracy theory.
- The recommended safeguards are:
  - guardrails in chatbot conversations;
  - evidence-backed answers;
  - retrieval-augmented LLMs and knowledge graphs for factual consistency;
  - algorithms that detect coordinated campaigns;
  - AI literacy programs for all age groups.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/) · [AI & machine learning](https://md.research.elamur.ai/free/en/shelves/ai/)

Topics: [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{augenstein2023factuality,
  title = {{Factuality Challenges in the Era of Large Language Models}},
  author = {Augenstein, Isabelle and Baldwin, Timothy and Cha, Meeyoung and Chakraborty, Tanmoy and Ciampaglia, Giovanni Luca and Corney, David and DiResta, Renee and Ferrara, Emilio and Hale, Scott and Halevy, Alon and Hovy, Eduard and Ji, Heng and Menczer, Filippo and Miguez, Ruben and Nakov, Preslav and Scheufele, Dietram and Sharma, Shivam and Zagni, Giovanni},
  year = {2023},
  doi = {10.1038/s42256-024-00881-z},
  eprint = {2310.05189},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.1038/s42256-024-00881-z}
}
```

- [← previous Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations](https://md.research.elamur.ai/free/en/papers/li-2023-synthetic-data-generation-large-language/)
- [next → In-context Examples Selection for Machine Translation](https://md.research.elamur.ai/free/en/papers/agrawal-2023-context-examples-selection-machine-translation/)
- [↑ back to papers of 2023](https://md.research.elamur.ai/free/en/papers/2023/page/3/)

## References

32 references, 8 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Shannon, C. E. 10.1002/j.1538-7305.1948.tb01338.x (1948). The Bell Syst. Tech. J. 27, 379–423, DOI: — [doi:10.1002/j.1538-7305.1948.tb01338.x](https://doi.org/10.1002/j.1538-7305.1948.tb01338.x) [find](https://scholar.google.com/scholar?q=Shannon%2C+C%2E+E%2E+10%2E1002%2Fj%2E1538%2D7305%2E1948%2Etb01338%2Ex+%281948%29%2E+The+Bell+Syst%2E+Tech%2E+J%2E+27%2C+379%E2%80%93423%2C+DOI%3A+%E2%80%94+%5Bdoi%3A10%2E1002%2Fj%2E1538%2D7305%2E1948%2Etb01338%2Ex%5D%28https%3A%2F%2Fdoi%2Eorg%2F10%2E1002%2Fj%2E1538%2D7305%2E1948%2Etb01338%2Ex%29)
2. Huang, J. & Chang, K. C.-C. Towards Reasoning in Large Language Models: A Survey. In Findings of the Association for Computational Linguistics: ACL 2023, 1049–1065, DOI: 10.18653/v1/2023.findings-acl.67 (Association for Computational Linguistics, Toronto, Canada, 2023). [find](https://scholar.google.com/scholar?q=Huang%2C+J%2E+%26+Chang%2C+K%2E+C%2E%2DC%2E+Towards+Reasoning+in+Large+Language+Models%3A+A+Survey%2E+In+Findings+of+the+Association+for+Computational+Linguistics%3A+ACL+2023%2C+1049%E2%80%931065%2C+DOI%3A+10%2E18653%2Fv1%2F2023%2Efindings%2Dacl%2E) [our card](https://md.research.elamur.ai/free/en/papers/huang-2023-reasoning-large-language-models-survey/)
3. Radford, A., Narasimhan, K., Salimans, T., Sutskever, I. et al. Improving language understanding by generative pre-training (2018). \#\# 4. OpenAI. GPT-4 technical report. arXiv:2303.08774 (2023). [find](https://scholar.google.com/scholar?q=Radford%2C+A%2E%2C+Narasimhan%2C+K%2E%2C+Salimans%2C+T%2E%2C+Sutskever%2C+I%2E+et+al%2E+Improving+language+understanding+by+generative+pre%2Dtraining+%282018%29%2E+%5C%23%5C%23+4%2E+OpenAI%2E+GPT%2D4+technical+report%2E+arXiv%3A2303%2E08774+%282023%29%2E) [our card](https://md.research.elamur.ai/free/en/papers/openai-2023-gpt-4-technical-report/)
4. Touvron, H. et al. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023). [find](https://scholar.google.com/scholar?q=Touvron%2C+H%2E+et+al%2E+Llama%3A+Open+and+efficient+foundation+language+models%2E+arXiv+preprint+arXiv%3A2302%2E13971+%282023%29%2E)
5. Introducing ChatGPT — openai.com. [https://openai.com/blog/chatgpt](https://openai.com/blog/chatgpt). [Accessed 27-09-2023]. [↗](https://openai.com/blog/chatgpt)
6. Hu, K. ChatGPT sets record for fastest-growing user base - analyst note. Reuters (2023). [find](https://scholar.google.com/scholar?q=Hu%2C+K%2E+ChatGPT+sets+record+for+fastest%2Dgrowing+user+base+%2D+analyst+note%2E+Reuters+%282023%29%2E)
7. Thoppilan, R. et al. Lamda: Language models for dialog applications. arXiv preprint arXiv:2201.08239 (2022). [find](https://scholar.google.com/scholar?q=Thoppilan%2C+R%2E+et+al%2E+Lamda%3A+Language+models+for+dialog+applications%2E+arXiv+preprint+arXiv%3A2201%2E08239+%282022%29%2E) [our card](https://md.research.elamur.ai/free/en/papers/thoppilan-2022-lamda-language-models-dialog-applications/)
8. Taori, R. et al. Stanford Alpaca: An instruction-following LLaMA model. [https://github.com/tatsu-lab/stanford_alpaca](https://github.com/tatsu-lab/stanford_alpaca) (2023). [↗](https://github.com/tatsu-lab/stanford_alpaca)
9. Vicuna: An open-source chatbot impressing GPT-4 [https://lmsys.org/blog/2023-03-30-vicuna/](https://lmsys.org/blog/2023-03-30-vicuna/). [Accessed 15-09-2023]. with 90%* ChatGPT quality. [↗](https://lmsys.org/blog/2023-03-30-vicuna/)
10. Introducing Claude. [https://www.anthropic.com/index/introducing-claude](https://www.anthropic.com/index/introducing-claude). [Accessed 15-09-2023]. \#\# 12. Falcon. https://falconllm.tii.ae/falcon.html. [Accessed 15-09-2023]. [↗](https://www.anthropic.com/index/introducing-claude)
11. Jurassic-2 models. [https://docs.ai21.com/docs/jurassic-2-models](https://docs.ai21.com/docs/jurassic-2-models). [Accessed 20-09-2023]. [↗](https://docs.ai21.com/docs/jurassic-2-models)
12. Sengupta, N. et al. Jais and Jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint 2308.16149 (2023). 2308.16149. [find](https://scholar.google.com/scholar?q=Sengupta%2C+N%2E+et+al%2E+Jais+and+Jais%2Dchat%3A+Arabic%2Dcentric+foundation+and+instruction%2Dtuned+open+generative+large+language+models%2E+arXiv+preprint+2308%2E16149+%282023%29%2E+2308%2E16149%2E) [our card](https://md.research.elamur.ai/free/en/papers/sengupta-2023-jais-jais-chat-arabic-centric/)
13. Radford, A. et al. Language models are unsupervised multitask learners. OpenAI blog 1, 9 (2019). [find](https://scholar.google.com/scholar?q=Radford%2C+A%2E+et+al%2E+Language+models+are+unsupervised+multitask+learners%2E+OpenAI+blog+1%2C+9+%282019%29%2E)
14. Brown, T. et al. Language Models are Few-Shot Learners. In Advances in Neural Information Processing Systems, vol. 33, 1877–1901 (Curran Associates, Inc., 2020). [find](https://scholar.google.com/scholar?q=Brown%2C+T%2E+et+al%2E+Language+Models+are+Few%2DShot+Learners%2E+In+Advances+in+Neural+Information+Processing+Systems%2C+vol%2E+33%2C+1877%E2%80%931901+%28Curran+Associates%2C+Inc%2E%2C+2020%29%2E)
15. Zhao, W. X. et al. A survey of large language models. arXiv preprint arXiv:2303.18223 (2023). [find](https://scholar.google.com/scholar?q=Zhao%2C+W%2E+X%2E+et+al%2E+A+survey+of+large+language+models%2E+arXiv+preprint+arXiv%3A2303%2E18223+%282023%29%2E) [our card](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/)
16. Li, S. et al. Open-domain hierarchical event schema induction by incremental prompting and verification. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 5677–5697, DOI: 10.18653/v1/2023.acl-long.312 (Association for Computational Li… [find](https://scholar.google.com/scholar?q=Li%2C+S%2E+et+al%2E+Open%2Ddomain+hierarchical+event+schema+induction+by+incremental+prompting+and+verification%2E+In+Proceedings+of+the+61st+Annual+Meeting+of+the+Association+for+Computational+Linguistics+%28Vol)
17. Wei, J. et al. Emergent abilities of large language models. Trans. Mach. Learn. Res. 2022 (2022). [find](https://scholar.google.com/scholar?q=Wei%2C+J%2E+et+al%2E+Emergent+abilities+of+large+language+models%2E+Trans%2E+Mach%2E+Learn%2E+Res%2E+2022+%282022%29%2E) [our card](https://md.research.elamur.ai/free/en/papers/wei-2022-emergent-abilities-large-language-models/)
18. Bang, Y. et al. A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity. Preprint 2302.04023, arXiv (2023). DOI: 10.48550/arXiv.2302.04023. [find](https://scholar.google.com/scholar?q=Bang%2C+Y%2E+et+al%2E+A+Multitask%2C+Multilingual%2C+Multimodal+Evaluation+of+ChatGPT+on+Reasoning%2C+Hallucination%2C+and+Interactivity%2E+Preprint+2302%2E04023%2C+arXiv+%282023%29%2E+DOI%3A+10%2E48550%2FarXiv%2E2302%2E04023%2E) [our card](https://md.research.elamur.ai/free/en/papers/bang-2023-multitask-multilingual-multimodal-evaluation-chatgpt/)
19. Bergstrom, C. T. & Ogbunu, C. B. ChatGPT isn’t ‘hallucinating.’ It’s Bullshitting. [https://undark.org/2023/04/06/chatgpt-isnt-hallucinating-its-bullshitting/](https://undark.org/2023/04/06/chatgpt-isnt-hallucinating-its-bullshitting/) (2023). Accessed 2 October 2023. [↗](https://undark.org/2023/04/06/chatgpt-isnt-hallucinating-its-bullshitting/)
20. Sison, A. J. G., Daza, M. T., Gozalo-Brizuela, R. & Garrido-Merchán, E. C. ChatGPT: More Than a “Weapon of Mass Deception” – Ethical Challenges and Responses from the Human-Centered Artificial Intelligence (HCAI) Perspective. Int. J. Human-Computer Interact. 0, 1–20, DOI: 10.1080/10447318.2023.22259… — [doi:10.1080/10447318.2023.2225931](https://doi.org/10.1080/10447318.2023.2225931) [find](https://scholar.google.com/scholar?q=Sison%2C+A%2E+J%2E+G%2E%2C+Daza%2C+M%2E+T%2E%2C+Gozalo%2DBrizuela%2C+R%2E+%26+Garrido%2DMerch%C3%A1n%2C+E%2E+C%2E+ChatGPT%3A+More+Than+a+%E2%80%9CWeapon+of+Mass+Deception%E2%80%9D+%E2%80%93+Ethical+Challenges+and+Responses+from+the+Human%2DCentered+Artificial+Intelli)
21. Iftikhar, L. et al. Docgpt: Impact of chatgpt-3 on health services as a virtual doctor. EC Paediatr. 12, 45–55 (2023). [find](https://scholar.google.com/scholar?q=Iftikhar%2C+L%2E+et+al%2E+Docgpt%3A+Impact+of+chatgpt%2D3+on+health+services+as+a+virtual+doctor%2E+EC+Paediatr%2E+12%2C+45%E2%80%9355+%282023%29%2E)
22. Chin, H. et al. User-chatbot conversations during the COVID-19 pandemic: Study based on topic modeling and sentiment analysis. J. Med. Internet Res. 25, e40922, DOI: 10.2196/40922 (2023). — [doi:10.2196/40922](https://doi.org/10.2196/40922) [find](https://scholar.google.com/scholar?q=Chin%2C+H%2E+et+al%2E+User%2Dchatbot+conversations+during+the+COVID%2D19+pandemic%3A+Study+based+on+topic+modeling+and+sentiment+analysis%2E+J%2E+Med%2E+Internet+Res%2E+25%2C+e40922%2C+DOI%3A+10%2E2196%2F40922+%282023%29%2E+%E2%80%94+%5Bdoi%3A10%2E21)
23. Palanica, A., Flaschner, P., Thommandram, A., Li, M. & Fossat, Y. Physicians’ perceptions of chatbots in health care: Cross-sectional web-based survey. J Med Internet Res 21, e12887, DOI: 10.2196/12887 (2019). — [doi:10.2196/12887](https://doi.org/10.2196/12887) [find](https://scholar.google.com/scholar?q=Palanica%2C+A%2E%2C+Flaschner%2C+P%2E%2C+Thommandram%2C+A%2E%2C+Li%2C+M%2E+%26+Fossat%2C+Y%2E+Physicians%E2%80%99+perceptions+of+chatbots+in+health+care%3A+Cross%2Dsectional+web%2Dbased+survey%2E+J+Med+Internet+Res+21%2C+e12887%2C+DOI%3A+10%2E2196%2F1288)
24. Peskoff, D. & Stewart, B. Credible without credit: Domain experts assess generative language models. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 427–438, DOI: 10.18653/v1/2023.acl-short.37 (Association for Computational Linguis… [find](https://scholar.google.com/scholar?q=Peskoff%2C+D%2E+%26+Stewart%2C+B%2E+Credible+without+credit%3A+Domain+experts+assess+generative+language+models%2E+In+Proceedings+of+the+61st+Annual+Meeting+of+the+Association+for+Computational+Linguistics+%28Volume+)
25. Srivastava, B. Did chatbots miss their “Apollo moment”? Potential, gaps, and lessons from using collaboration assistants during COVID-19. Patterns 2, 100308, DOI: https://doi.org/10.1016/j.patter.2021.100308 (2021). — [doi:10.1016/j.patter.2021.100308](https://doi.org/10.1016/j.patter.2021.100308) [↗](https://doi.org/10.1016/j.patter.2021.100308)
26. Menczer, F., Crandall, D., Ahn, Y.-Y. & Kapadia, A. Addressing the harms of AI-generated inauthentic content. Nat. Mach. Intell. 5, 678–680, DOI: 10.1038/s42256-023-00690-w (2023). — [doi:10.1038/s42256-023-00690-w](https://doi.org/10.1038/s42256-023-00690-w) [find](https://scholar.google.com/scholar?q=Menczer%2C+F%2E%2C+Crandall%2C+D%2E%2C+Ahn%2C+Y%2E%2DY%2E+%26+Kapadia%2C+A%2E+Addressing+the+harms+of+AI%2Dgenerated+inauthentic+content%2E+Nat%2E+Mach%2E+Intell%2E+5%2C+678%E2%80%93680%2C+DOI%3A+10%2E1038%2Fs42256%2D023%2D00690%2Dw+%282023%29%2E+%E2%80%94+%5Bdoi%3A10%2E1038%2Fs422)
27. Patel, A. & Sattler, J. Creatively malicious prompt engineering. Tech. Rep., WithSecure Labs (2023). [find](https://scholar.google.com/scholar?q=Patel%2C+A%2E+%26+Sattler%2C+J%2E+Creatively+malicious+prompt+engineering%2E+Tech%2E+Rep%2E%2C+WithSecure+Labs+%282023%29%2E)
28. Brewster, J., Wang, M. & Palmer, C. Plagiarism-Bot? How Low-Quality Websites Are Using AI to Deceptively Rewrite [https://www.newsguardtech.com/misinformation-monitor/august-2023/](https://www.newsguardtech.com/misinformation-monitor/august-2023/) Content from Mainstream News Outlets. (2023). [Accessed 23 Sep 2023]. [↗](https://www.newsguardtech.com/misinformation-monitor/august-2023/)
29. Yang, K.-C. & Menczer, F. Anatomy of an AI-powered malicious social botnet. Preprint 2307.16336, arXiv (2023). DOI: 10.48550/arXiv.2307.16336. — [doi:10.48550/arxiv.2307.16336](https://doi.org/10.48550/arxiv.2307.16336) [find](https://scholar.google.com/scholar?q=Yang%2C+K%2E%2DC%2E+%26+Menczer%2C+F%2E+Anatomy+of+an+AI%2Dpowered+malicious+social+botnet%2E+Preprint+2307%2E16336%2C+arXiv+%282023%29%2E+DOI%3A+10%2E48550%2FarXiv%2E2307%2E16336%2E+%E2%80%94+%5Bdoi%3A10%2E48550%2Farxiv%2E2307%2E16336%5D%28https%3A%2F%2Fdoi%2Eorg%2F10%2E4855)
30. Verma, P. & Oremus, W. ChatGPT invented a sexual harassment scandal and named a real law prof as the accused. Wash. Post (2023). [find](https://scholar.google.com/scholar?q=Verma%2C+P%2E+%26+Oremus%2C+W%2E+ChatGPT+invented+a+sexual+harassment+scandal+and+named+a+real+law+prof+as+the+accused%2E+Wash%2E+Post+%282023%29%2E)
31. DeVerna, M. R., Yan, H. Y., Yang, K.-C. & Menczer, F. Artificial intelligence is ineffective and potentially harmful for fact checking. Preprint 2308.10800, arXiv (2023). DOI: 10.48550/arXiv.2308.10800. — [doi:10.48550/arxiv.2308.10800](https://doi.org/10.48550/arxiv.2308.10800) [find](https://scholar.google.com/scholar?q=DeVerna%2C+M%2E+R%2E%2C+Yan%2C+H%2E+Y%2E%2C+Yang%2C+K%2E%2DC%2E+%26+Menczer%2C+F%2E+Artificial+intelligence+is+ineffective+and+potentially+harmful+for+fact+checking%2E+Preprint+2308%2E10800%2C+arXiv+%282023%29%2E+DOI%3A+10%2E48550%2FarXiv%2E2308%2E1080)
32. Ferrara, E. The history of digital spam. Commun. ACM 62, 82–91, DOI: 10.1145/3299768 (2019). \#\# 35. Vincent, J. Google’s AI chatbot Bard makes factual error in first demo (2023). \#\# 36. Marcus, G. Deep Learning Is Hitting a Wall (2022). 37. Ferrara, E. Should ChatGPT be Biased? Challenges and Risks… [find](https://scholar.google.com/scholar?q=Ferrara%2C+E%2E+The+history+of+digital+spam%2E+Commun%2E+ACM+62%2C+82%E2%80%9391%2C+DOI%3A+10%2E1145%2F3299768+%282019%29%2E+%5C%23%5C%23+35%2E+Vincent%2C+J%2E+Google%E2%80%99s+AI+chatbot+Bard+makes+factual+error+in+first+demo+%282023%29%2E+%5C%23%5C%23+36%2E+Marcus%2C+G%2E) [our card](https://md.research.elamur.ai/free/en/papers/wang-2018-glue-multi-task-benchmark-analysis/)