# What do academics think LLM ‘hallucination’ means?

What does the word ‘hallucination’ actually mean in reference to LLMs when used by academics? And where does that word come from? I’ve trawled the academic literature for answers.

Published on: 16 Jul 2024
Updated on: 17 Jul 2024

The word, ‘hallucination’ as applied to LLMs has me enthralled right now. It’s such a *weird* word. And problematic, I think.

If you don’t know what I’m on about, have a quick look at IBM’s introduction to the topic. It gives you a good intuition what people are talking about, and helpfully illustrates the deep conceptual confusions which have me gripped.

=> https://www.ibm.com/topics/ai-hallucinations

But this got me asking: what do people actually mean when they say that an LLM is ‘hallucinating’? And why that word? Where does it come from, and how did it end up in our mouths?

As far as I could see, nobody seems to have answered this question before, except with the odd bit of unsubstantiated guessing. I think it’s important that we know where this word came from and what it means.

So I ended up going on a bit of a treasure hunt. I’ve skimmed well over a hundred academic articles to trace where the word came from, how it’s been used over time, and how it’s spread from field to field.

The result is that I have what I hope is a pretty compelling story of where this weird old word come from and what it’s supposed to mean. I’ve also got an excruciating amount of evidence.

What I’ll do, is first present my story, and then step through the excruciating detail, providing you with all my sources, so you can check my working, and perhaps see what important evidence I might have missed. If you’re just here for the headlines, you don’t have to read the excruciating bits.

Before I proceed, two caveats.

1. I am not a subject matter expert. Judge the quality of my arguments and my evidence. Do not treat me as an authority.
2. For the purposes of this post, I’m just considering academic literature. The ways that people use the word ‘hallucination’ in popular discourse may be quite different. (For the record, I think they do: but that’s a story for another day.)

## The long and short of it

So this is my theory.

In 1999, the word ‘hallucination’ was coined accidentally and off-hand by a couple of researchers, Baker and Kanade, in the field of computer vision (CV).

The word caught on in CV, and came to have a fairly unambiguous meaning. Given an image with missing pixels you want to fill in, ‘hallucinating’ meant generating those missing pixels by means of information in a deep learning model’s training data, rather than by means of any information contained in the input image. (This was also known as ‘example-based in-painting’). It was a term of art, not jargon.

Then, sometime between 2017 and the end of 2018, a few different researchers in natural language processing (NLP) adopted the term. They were probably aware of the term's usage in CV, and transferred it to mean something similar in NLP.

When these NLP researchers used it, they used it to refer to when a model produces content which is irrelevant to the input, or contains information not contained in the input.

When the word ‘hallucination’ first appeared in two or three papers in NLP, the authors used it as a technical term for their own limited purposes. However, it was soon adopted in various contexts for similar concepts.

For example, in image captioning, ‘hallucination’ came to mean producing a caption which mentions an object which wasn’t depicted in the input image. In machine translation, it came to mean generating a translation which was in the right language, but which meant something completely irrelevant to the input. In abstractive summarisation, it came to mean producing a summary containing details which weren’t in the text the model was supposed to summarise.

When the term entered NLP, it underwent a striking transformation. It acquired a *valence*.

In CV, the term referred to normal behaviour, and was value-neutral. It was just a description of what a certain machine does. ‘We need to fill in these missing pixels. The model will do that by hallucinating them.’

In contrast, in NLP, the term ‘hallucination’ was always used to describe *abnormal* behaviour, and almost always used with a negative valency. In other words, while CV folk used it to describe what they expected their models to do, NLP folk used it to describe a *bug*.

(There were odd exceptions, though. You do get the periodic paper which insists that there are positive applications of hallucination. But the fact they have to insist so hard rather proves that that boat has already sailed. And even they are still buying in to the idea that hallucination is abnormal behaviour.)

Now, that brings us up to about 2020. From 2020, we get another change.

From about 2020 until the present, academics have attempted to *jargonise* the word ‘hallucination’. Broadly, they have so far *failed*.

When I say that people have attempted to ‘jargonise’ the word, I mean that people are using it while assuming a specific definition. They are using it with technical qualifiers like ‘extrinsic’. They are using it without explaining the meaning or the context, as if you should already know what the word means. And ‘hallucination’ itself is becoming an object of study.

When I say that they have ‘failed’, I mean that there is no widely agreed and specific meaning for the word, which in my mind, is a crucial feature of jargon. However, the intended meanings tend to coalesce around the idea of an abnormal behaviour of LLMs whereby they produce output which is not epistemically supported by their input.

## Excruciata

OK, that was the long and short of it. Now for the excruciating detail. To recap, I think these six claims are enough to support my theory:

1. Since 2022, academics have treated ‘hallucination’ as jargon
2. Since 2017/18, the word ‘hallucination’ has typically been used to describe LLMs producing unfaithful output
3. Up to now, academics have not agreed a specific meaning for the word ‘hallucination’
4. The word ‘hallucination’ entered the NLP literature around 2017/2018, probably from CV
5. The word ‘hallucination’ was not jargon in CV, but was a term of art for example-based in-painting
6. The word ‘hallucination’ entered the CV literature in Baker & Kanade 1999

I’ll work through each of these in turn, showing how I got there from the available evidence.

Feel free to use this as a cheap way to harvest citations for your own research.

### 1. Since 2022, academics have treated ‘hallucination’ as jargon

My evidence for this is twofold: surveys of ‘hallucination’ usually treat the word as jargon, and many original research papers do, too.

First, let’s look at those surveys. These first five all belong together.

- Ji et al 2022 [^16] - Liu et al 2023 [^30] - Rawte et al 2023 [^42] - Wang, Cunxiang et al 2023 [^48] - Huang, Lei et al 2023 [^13]

All the latter four of these surveys lean heavily on Ji et al 2022 for their definition of ‘hallucination’.  Since they all source their definition of ‘hallucination’ from Ji et al 2022, it follows that their definitions are all extremely similar, and often verbatim. Ji et al 2022 had this to say about the word ‘hallucination’:

> Within the context of NLP, the most inclusive and standard definition of > hallucination is the \[sic\] generation that is nonsensical or unfaithful to > the provided source content.

They also made a distinction between *intrinsic* hallucination, which they regarded as ‘the generation output that contradicts the source content’, and *extrinsic* hallucination, which they regarded as ‘the generation output that cannot be verify \[sic\] with the source content’. Some of the other surveys pick up on this intrinsic/extrinsic distinction. To me, extending the word with technical epithets suggests they regard the word itself as already having an agreed, specific meaning.

Meanwhile, Li, Wei et al 2022 [^26], another survey, offers no definition of ‘hallucination’, but uses it freely along with the intrinsic/extrinsic distinction. This suggests they expect their expert readers to already know some agreed meaning of the word ‘hallucination’. This is a hallmark of jargon in my books.

Original research also shows the word ‘hallucination’ being used as jargon. For example, Maynez et al 2020 [^35], despite being often cited when other authors first introduce the term ‘hallucination’, provides no definition of the term. They do, however, use the intrinsic/extrinsic distinction (this might be the paper which coined the distinction).

All the following papers use the word ‘hallucination’, sometimes in passing comments, sometimes as the principal focus of the paper, without attempting to define the word.

- Mao et al 2020 [^32] - Wang, Alex et al 2020 [^47] - Lin et al 2022 [^29] - Kumar et al 2022 [^21] - Lee, Hwanhee et al 2022 [^23] - Li, Junyi et al 2023 [^27] - Guerreiro et al 2023 [^12] - Dahl et al 2024 [^6] - Song et al 2024 [^46]

Dahl et al 2024 also use the word without defining it, even as they tack on their own bespoke technical epithets, ‘open-domain’ and ‘closed-domain’. Plus, in many of these examples, the context is not enough to make clear what the word ‘hallucination’ is taken to mean.

All these examples demonstrate that the authors assume that their audience, *viz* technical experts, will come with an agreed and specific meaning of the word ‘hallucination’ pre-baked and ready for precise academic application.

In other words, these authors, including both surveys and primary literature, use the word ‘hallucination’ as if it is jargon.

### 2. Since 2017/18, the word ‘hallucination’ has typically been used to describe LLMs producing unfaithful output

Recall those surveys I referred to before. They mostly depended on Ji et al 2022’s definition, which was:

> \[...\] generation that is nonsensical or unfaithful to the provided source > content. [^16]

Whatever we might think about ‘hallucination’, the word ‘unfaithful’ really is a jargon word in the field of natural language processing, with an agreed, specific meaning. An LLM is ‘unfaithful’ just in case it produces output which is not epistemically supported by the input.

This is most often used in the case of summarisers. In that case, a summariser is unfaithful just in case it produces a summary containing information which is not implied by any information in the document it was supposed to summarise.

(‘Nonsensical’ is not a jargon term as far as I know. I have never encountered any attempt in the NLP literature to define ‘nonsensical’, and although this definition is often quoted, the term ‘nonsensical’ is rarely used in practice.)

Now, I am not claiming that this is the only way or even the dominant way in which the term ‘hallucination’ has been used. Indeed, in section 3, I’m going to show precisely that this is *not* the case: in fact, the word ‘hallucination’ continues to be used in diverse ways in the academic literature.

All I want to claim here is that it is *typical* for academics to use the word ‘hallucination’ to mean something in the rough area of unfaithfulness.

This is an important part of the story, because this is the meaning which links the word most closely to its etymological roots in computer vision.

All I need to do really is provide enough citations. I know I haven’t read everything. But if what was typical in my reading is unusual in the literature at large, I’ve been the victim of some extraordinary bad luck. Have a look at these papers:

* Durmus et al 2020 [^7] defines ‘hallucination’ as one of two kinds of failures of faithfulness 
* Huang, Luyang et al 2020 [^14] defines ‘hallucination’ as ‘creating content not present in the input’ 
* Maynez et al 2020 [^35] introduces ‘hallucination’ as a cause of unfaithfulness 
* Zhao et al 2020 [^56] defines ‘hallucination’ as ‘including material that is not supported by the original text’ 
* Nan et al 2021 [^38] defines ‘hallucination’ as putting out claims not supported by the input 
* Zhou, Chunting et al 2021 [^59] defines ‘hallucination’ as failures of faithfulness in machine translation 
* Mao et al 2020 [^32] clearly uses ‘hallucination’ to mean unfaithfulness 
* King et al 2022 [^18] is aware of usages of ‘hallucination’ to refer to unfactuality, but narrows in on failures of faithfulness, which they call ‘consistency’

So there you have it. ‘Hallucination’ has widely been taken to roughly mean ‘unfaithfulness’.

### 3. Up to now, academics have not agreed a specific meaning for the word ‘hallucination’

So we know that it was normal for academics to use ‘hallucination’ to mean something like ‘unfaithfulness’. But was it also normal for academics to use it for other meanings?

I think it was. There are a number of ways which authors used the word which are not consistent with the ‘unfaithfulness’ interpretation.

The main way is that authors have assumed that in order to be a ‘hallucination’, the output has to *contradict* something: either the input, or the training data, or the facts, or itself. This contrasts with the ‘faithfulness’ interpretation, under which a hallucination can be perfectly consistent with the input, the training data, the facts, and itself, as long as it isn’t *supported* by the input.

All the following papers are clear that hallucination requires contradicting something, usually the facts:

* Huang, Yichong et al 2021 [^15] 
* Zhu et al 2021 [^60] 
* Pagnoni et al 2021 [^40] 
* Shuster et al 2021 [^45] 
* Zhang, Yue et al 2023 [^55] 
* Rawte et al 2023 [^42] 
* Dahl et al 2024 [^6] 
* Magesh et al 2024 [^31]

On the other end of the spectrum, a few authors seemed to believe that hallucinated outputs *cannot* contradict the corresponding inputs. (This is also inconsistent with the ‘unfaithfulness’ view.)

* Durmus et al 2020 [^7] 
* Huang, Luyang et al 2020 [^14] 
* Nan et al 2021 [^38]

There’s also disagreement on whether ‘nonsense’, or ‘incoherence’, counts as hallucination.

On the one hand, Durmus et al 2020 [^7] argue that nonsensical outputs are not hallucinations, since it doesn’t make sense to ask whether nonsensical outputs are faithful. Likewise, Shuster et al 2021 [^45] contrast hallucination with incoherence.

But on the other hand, Pagnoni et al 2021 [^40] include misleading and incorrect grammar in their definition of ‘hallucination’, while Ji et al 2022 [^16] explicitly include ‘nonsensical’ outputs in their widely-quoted definition of ‘hallucination’.

Some authors have managed to recognise the ambiguity of the term. Both King et al 2022 [^18] and Farquhar et al 2024 [^9] acknowledge that the word ‘hallucination’ has been used variably to cover failures of faithfulness and failures of factuality, and are careful to define their terms for their own purposes to avoid being misunderstood in the context of this ambiguity.

So it seems that although many academics have treated the term ‘hallucination’ as jargon, in actual fact, there is no widely agreed specific meaning of the word.

---

I will slide in here with a quick side note. While some authors treated ‘hallucination’ as jargon for unfaithfulness or unfactuality, other authors contemporaneously managed to talk about these topics without using the word ‘hallucination’ at all. Here are some examples:

* Cao, Ziqiang et al 2018 [^3] 
* Li, Haoran et al 2018 [^25] 
* Falke et al 2019 [^8] 
* Goodrich et al 2019 [^11] 
* Kryściński et al 2019 [^20] 
* Cao, Meng et al 2020 [^4] 
* Zhang, Yuhao et al 2019 [^54] 
* Marcus & Davis 2020 [^33] 
* Marcus 2020 [^34] 
* Krishna et al 2021 [^19] 
* Bai et al 2022 [^1] 
* Weidinger et al 2022 [^50] 
* Perez et al 2022 [^41] 
* Min et al 2023 [^36] 
* Muhlgay et al 2024 [^37]

Probably not a significant enough point to merit a whole section. But there you are. I think it adds to the picture that ‘hallucination’ is failed jargon.

### 4. The word ‘hallucination’ entered the NLP literature around 2017/2018, probably from CV

So people have been using the word ‘hallucination’ in the NLP literature recently. But where did it come from?

I think it entered the NLP literature somewhere around 2017/18, and probably was borrowed from the field of computer vision (CV).

I can't offer a theory for a single, original usage of the word in NLP. But there are three papers I feel are pretty close. These are the three oldest papers I could find in the NLP literature which use the word ‘hallucination’.

* Wiseman et al 2017 [^51] 
* Rohrbach et al 2018 [^44] 
* Lee, Katherine et al 2018 [^22]

Both Rohrbach et al and Lee et al use a form of language which suggests they are intentionally coining a technical term. Here’s Rohrbach et al:

> In Figure 1 we show an example where a competitive captioning model, Neural > Baby Talk (NBT) (Lu et al., 2018), incorrectly generates the object “bench.” > We refer to this issue as object *hallucination*.

And here’s Lee et al:

> These mistranslations are completely semantically incorrect and also > grammatically viable. They are untethered from the input so we name them > *‘hallucinations’*.

I have no reason to believe that these two papers are deliberately plagiarising each other. It’s reasonable to assume that these two papers genuinely coined a similar term for a similar phenomenon at the same time.

Wiseman et al 2017, in contrast, doesn’t look like it’s attempting to coin a technical term. They just use the word once, presumably as a stylistic flourish to help illustrate their point. But they do use it for a closely related concept.

> \[…\] we see the model hallucinates factual statements, such as “in front of > their home crowd,” which is presumably likely according to the language model, > but ultimately incorrect (and not supported by anything in the box- or line- > scores).

Just to add to the picture, Ehud Reiter, in his 2018 blog post [^43], gives us an insight into the International Natural Language Generation conference of 2018. He claims that at that conference, ‘hallucination’ was a hot topic of discussion, and cites Rohrbach 2018 to support his claim that ‘hallucination is a well-known problem in neural approaches to image captioning’.

It might be fruitful to have a peruse of the [INLG 2018 Proceedings](https://aclanthology.org/events/inlg-2018) to see whether that interest in ‘hallucination’ was reflected in the written contributions, or if it was mainly contained in verbal discussions. I haven’t taken the liberty to do this myself: by all means, have a look yourself and let me know what you find!

I think all this suggests that the word ‘hallucination’ entered the field gradually, not with a bang, and perhaps spread by word of mouth at conferences as much as it spread through published papers, at least at first.

But we still have to explain why all these different authors seem to have independently come up with a similar meaning for the word ‘hallucination’.

I think the best explanation is that they got the word ‘hallucination’ from computer vision (CV). If my fifth section is cogent, then ‘hallucination’ was used in CV to refer to deep learning models generating data based on information in their training data, not from information contained in or implied by the input. This would explain how independent authors in NLP all independently coined the word ‘hallucination’ in their own field to mean pretty similar things. They were probably aware of the usage in the CV literature and adopted it by analogy.

So, I reckon the word ‘hallucination’ probably entered the NLP literature from a few authors independently, and they chose that word because they were borrowing it from the CV literature, where it was already being used for a similar concept.

But that depends on my next claim: so let’s look at that!

### 5. The word ‘hallucination’ was not jargon in CV, but was a term of art for example-based in-painting

I want to argue that ‘hallucination’, although it wasn’t jargon, was widely used in the computer vision (CV) literature to refer to what was more technically called ‘example-based in-painting’, that is, filling in gaps in images using the information from training data baked into neural networks.

First look at Baker & Kanade 1999 [^2]. If my sixth section is correct, this is where the term entered CV, but that’s not crucial to my argument here. Even if I’ve missed some crucial evidence, it is nonetheless pretty certain that they had a seminal effect on the use of the word in the field – they are early in the field’s history, and widely cited by other CV papers which use the word ‘hallucination’. They’re even cited as the origin of the term in machine learning by the NLP paper, Farquhar et al 2024 [^9], a quarter of a century later.

Baker & Kanade are writing about a new algorithm they’ve used in order to increase the resolution of low-resolution images of human faces. The intended application is for surveillance camera footage. They use the word ‘hallucination’ once in their title (‘Hallucinating faces’), once in their abstract, and 79 times in the main body of the text.

Across those 79 occurrences, they are using it to refer to one of three things:

* Their algorithm: *eg_ their ‘face hallucination algorithm’ 
* The output of their algorithm: _eg_ ‘hallucinated faces’ 
* What their activity does: _eg* ‘a face is hallucinated by our algorithm’

The closest they come to explaining what they *mean* by the word is in their abstract:

> Although numerous resolution enhancement algorithms have been proposed in the > literature, most of them are limited by the fact that they make weak, if any, > assumptions about the scene. We propose an algorithm that can be used to learn > a prior on the spatial distribution of the image gradient for frontal images > of faces. We proceed to show how such a prior can be incorporated into a > super-resolution algorithm to yield 4-8 fold improvements in resolution (16-64 > times as many pixels) using as few as 2-3 images. The additional pixels are, > in effect, hallucinated.

(By ‘super-resolution’, they mean increasing the resolution of images. This is typically reduced to a particular kind of ‘image in-painting’ problem, which in general means filling in gaps in images.)

So, their idea is that, rather than limiting your algorithm to the information contained in the input image, you can get better results by baking in assumptions about the image to the algorithm.

Why is this supposed to work? They hope that for a clever enough algorithm, the information that ‘this blurry splodge is a full-frontal portrait of a human face’ will provide just enough information to accurately guess what a higher-resolution version of the image would have been.

As for how that information, about what full-frontal portraits of human faces look like, gets baked into the algorithm in practice: that’s done by training a neural network on full-frontal images of faces. Hence why this field was so close to NLP, which, in recent years at least, has leaned heavily on neural networking or ‘deep learning’ approaches.

To recap, they don’t provide a specific meaning, but they do use it to roughly mean the process of inventing missing pixels in images, not on the basis of information contained in the input, but based on information contained in training data instead.

To see how this term was picked up by later authors in the field working on super-resolution, in-painting and other related tasks, see:

* Criminisi 2004 [^5] 
* Fawzi et al 2016 [^61] 
* Nazeri et al 2019 [^39] 
* Xiong et al 2019 [^53] 
* Xiang et al 2022 [^52]

But also notice other papers, on similar topics, which get on just fine without using the term at all:

* Karras et al 2017 [^17] 
* Zhou, Bolei et al 2017 [^58] 
* Liao et al 2018 [^28]

And notice how Wang, Zhihao et al 2020 [^49] introduce the word, as an alternative to another technical term:

> Face image super-resolution, a.k.a. face hallucination (FH), can often help > other face-related tasks

The fact that nobody ever attempts to define the word ‘hallucination’, and it doesn’t appear to be required lingo in any field, no matter how niche, to me implies that it wasn’t treated as jargon in CV, at least during the period from 1999 until 2018ish. (The usage in reference to LLMs may well have gone back to affect the usage in CV: I haven’t checked.)

So again. The word ‘hallucination’ was used in CV roughly to mean filling in gaps in images by means of information contained in training data, baked into an algorithm by training a neural network on many examples. It was used widely, but not universally, and was not treated as jargon.

### 6. The word ‘hallucination’ entered the CV literature in Baker & Kanade 1999

I think that the word ‘hallucination’ entered the CV literature in Baker & Kanade 1999. My argument for this is pretty straightforward.

I found the word ‘hallucination’ in Baker & Kanade 1999 [^2]. I couldn’t find it in CV anywhere earlier.

To add to that, they don’t use the word ‘hallucination’ in a way which suggests that they intentionally borrowed it from somewhere else.

My best theory is that they wanted a name for their algorithm to mark it out from the competition, and the glove fit. I think that’s plausible enough, and it’s consistent with the way Baker & Kanade use the word.

It’s also worth noting that according to Zhiwei et al 2009 [^57], the foundational work in the field was published only in the same year (*viz* Freeman & Pasztor 1999 [^10]). So there wasn’t really anywhere else for the word to have come *from*. The only possibility is that they borrowed the word from another field. If anybody reading this is able to suggest other fields which used the word ‘hallucination’ before 1999, please let me know!

## Congratulations

If you’ve made it this far, you are a *hero*. Pat yourself on the back. Please send me your corrections!

---

## References

I have made little to no attempt to normalise these references into a standard citation format. Please don’t tell the citation police.

1. Bai et al 2022. Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback. arXiv:2204.05862v1 \[cs.CL\] 12 Apr 2022

=> https://arxiv.org/abs/2204.05862

2. Baker, Simon & Kanade, Takeo 1999. Hallucinating Faces. Tech. Report, CMU-RI-TR-99-32, Robotics Institute, Carnegie Mellon University, September, 1999

=> https://www.ri.cmu.edu/publications/hallucinating-faces-2

3. Cao, Ziqiang et al 2018. Faithful to the Original: Fact Aware Neural Abstractive Summarization. The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18)

=> https://cdn.aaai.org/ojs/11912/11912-13-15440-1-2-20201228.pdf

4. Cao, Meng et al 2020. Factual Error Correction for Abstractive Summarization Models. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, pp 6251–6258, November 16–20, 2020

=> https://aclanthology.org/2020.emnlp-main.506.pdf

5. Criminisi, Perez & Toyama, "Region filling and object removal by exemplar-based image inpainting," in IEEE Transactions on Image Processing, vol. 13, no. 9, pp 1200-1212, Sept 2004, doi: 10.1109/TIP.2004.833105

=> https://doi.org/10.1109/TIP.2004.833105

6. Dahl et al 2024. Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models. arXiv:2401.01301v1 \[cs.CL\] 2 Jan 2024

=> https://arxiv.org/abs/2401.01301v1

7. Durmus et al 2020. FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarization. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp 5055–5070, July 5 - 10, 2020

=> https://aclanthology.org/2020.acl-main.454.pdf

8. Falke et al 2019. Ranking Generated Summaries by Correctness: An Interesting but Challenging Application for Natural Language Inference. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp 2214–2220, Florence, Italy, July 28 - August 2, 2019

=> https://aclanthology.org/P19-1213.pdf

9. Farquhar, S., Kossen, J., Kuhn, L. et al 2024. Detecting hallucinations in large language models using semantic entropy. Nature 630, 625–630 (2024). 19 June 2024. https://doi.org/10.1038/s41586-024-07421-0

=> https://doi.org/10.1038/s41586-024-07421-0

10. Freeman, W. T., & Pasztor, E. C. (1999). Learning low-level vision. Proceedings of the Seventh IEEE International Conference on Computer Vision. doi:10.1109/iccv.1999.790414

=> https://doi.org/10.1109/iccv.1999.790414

11. Goodrich et al 2019. Assessing The Factual Accuracy of Generated Text. arXiv:1905.13322v1 \[cs.CL\] 30 May 2019

=> https://arxiv.org/abs/1905.13322v1

12. Guerreiro et al 2023. Hallucinations in Large Multilingual Translation Models. arXiv:2303.16104v1 \[cs.CL\] 28 Mar 2023

=> https://arxiv.org/abs/2303.16104v1

13. Huang, Lei et al 2023. A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions. arXiv:2311.05232v1 \[cs.CL\] 9 Nov 2023

=> https://arxiv.org/abs/2311.05232v1

14. Huang, Luyang et al 2020. Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Reward. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp 5094–5107, July 5-10, 2020

=> https://aclanthology.org/2020.acl-main.457.pdf

15. Huang, Yichong et al 2021. The Factual Inconsistency Problem in Abstractive Text Summarization: A Survey. arXiv:2104.14839v1 \[cs.CL\] 30 Apr 2021

=> https://arxiv.org/abs/2104.14839v1

16. Ji et al 2022. Survey of Hallucination in Natural Language Generation. arXiv:2202.03629v1 \[cs.CL\] 8 Feb 2022

=> https://arxiv.org/abs/2202.03629v1

17. Karras et al 2017. PROGRESSIVE GROWING OF GANS FOR IMPROVED QUALITY, STABILITY, AND VARIATION. arXiv:1710.10196v3 \[cs.NE\] 26 Feb 2018

=> https://arxiv.org/abs/1710.10196v3

18. King et al 2022. Don’t Say What You Don’t Know: Improving the Consistency of Abstractive Summarization by Constraining Beam Search. arXiv:2203.08436v1 \[cs.CL\] 16 Mar 2022

=> https://arxiv.org/abs/2203.08436v1

19. Krishna, Roy & Iyyer 2021. Hurdles to Progress in Long-form Question Answering. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp 4940–4957, June 6–11, 2021

=> https://aclanthology.org/2021.naacl-main.393.pdf

20. Kryściński et al 2019. Evaluating the Factual Consistency of Abstractive Text Summarization. arXiv:1910.12840v1 \[cs.CL\] 28 Oct 2019

=> https://arxiv.org/abs/1910.12840v1

21. Kumar et al 2022. Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey. arXiv:2210.07700v1 \[cs.CL\] 14 Oct 2022

=> https://arxiv.org/abs/2210.07700v1

22. Lee, Katherine et al 2018. Hallucinations in Neural Machine Translation. Conference on Neural Information Processing Systems (NeurIPS 2018), Montréal, Canada

=> https://openreview.net/pdf?id=SJxTk3vB3m

23. Lee, Hwanhee et al 2022. Factual Error Correction for Abstractive Summaries Using Entity Retrieval. Proceedings of the 2nd Workshop on Natural Language Generation, Evaluation, and Metrics (GEM), pp 439-444, December 7, 2022

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