Why Large Language Models Hallucinate

Summary

The video script discusses the phenomenon of 'hallucinations' in large language models (LLMs), which are outputs that deviate from factual accuracy or contextual logic. It explains that these can range from minor inconsistencies to complete fabrications. The causes of hallucinations are explored, including data quality issues, the black box nature of LLM generation methods, and the importance of input context. To mitigate these issues, the video suggests providing clear and specific prompts, using active mitigation strategies such as adjusting the temperature parameter, and employing multi-shot prompting to give the LLM a better understanding of the desired output. The goal is to reduce hallucinations and leverage the full potential of LLMs while maintaining accuracy and relevance in their responses.

Takeaways

• 🌌 The script discusses the concept of 'hallucinations' in large language models (LLMs), which are outputs that deviate from factual accuracy or contextual logic.
• 🚀 The first 'fact' mentioned is incorrect; the distance from the Earth to the Moon is not 54 million kilometers, which is actually the approximate distance to Mars.
• 🎓 The second 'fact' is a personal mix-up; the speaker's brother, not the speaker, worked at an Australian airline.
• 🔭 The third 'fact' is also incorrect; the James Webb Telescope was not responsible for the first pictures of an exoplanet outside our solar system, which was actually achieved in 2004.
• 🤖 LLMs can generate fluent and coherent text but are prone to generating plausible-sounding but false information.
• ⛓ Hallucinations in LLMs can range from minor inconsistencies to major factual errors and can be categorized into different levels of severity.
• 🔍 The causes of hallucinations include data quality issues, where the training data may contain inaccuracies, biases, or inconsistencies.
• 📚 LLMs may generalize from unreliable data, leading to incorrect outputs, especially on topics not well-covered in the training data.
• 🤖 Generation methods like beam search and sampling can introduce biases and tradeoffs that affect the accuracy and novelty of LLM outputs.
• ➡️ Providing clear and specific prompts to an LLM can help reduce hallucinations by guiding the model towards more accurate and relevant outputs.
• 🔧 Employing active mitigation strategies, such as adjusting the temperature parameter, can help control the randomness of LLM outputs and minimize hallucinations.
• 📈 Multi-shot prompting, which involves giving the LLM multiple examples of the desired output, can improve the model's understanding and reduce the likelihood of hallucinations.

Q & A

What is the common thread among the three facts mentioned in the transcript?

The common thread is that all three statements are examples of hallucinations by a large language model (LLM), which are outputs that deviate from facts or contextual logic.

What is the actual distance from the Earth to the Moon?

The actual distance from the Earth to the Moon is not 54 million kilometers; that distance is typically associated with Mars. The average distance from the Earth to the Moon is about 384,400 kilometers.

What is a hallucination in the context of large language models?

A hallucination in the context of LLMs refers to outputs that are factually incorrect, inconsistent with the context, or completely fabricated. These can range from minor inaccuracies to major contradictions.

Why are large language models prone to hallucinations?

LLMs are prone to hallucinations due to several factors, including the quality of the training data, which may contain errors or biases, the generation methods used that can introduce biases, and the input context provided by users, which can be unclear or contradictory.

How can providing clear and specific prompts help reduce hallucinations in LLMs?

Clear and specific prompts help guide the LLM to generate more relevant and accurate outputs by giving the model a better understanding of the expected information and context in the response.

What is the role of context in generating outputs from LLMs?

Context is crucial as it helps guide the model to produce relevant and accurate outputs. However, if the context is unclear, inconsistent, or contradictory, it can lead to hallucinations or incorrect outputs.

What are some strategies to minimize hallucinations when using LLMs?

Strategies to minimize hallucinations include providing clear and specific prompts, using active mitigation strategies like adjusting the temperature parameter to control randomness, and employing multi-shot prompting to give the model multiple examples of the desired output format or context.

How does the temperature parameter in LLMs affect the output?

The temperature parameter controls the randomness of the output. A lower temperature results in more conservative and focused responses, while a higher temperature leads to more diverse and creative outputs, but also increases the chance of hallucinations.

What is multi-shot prompting and how does it help in reducing hallucinations?

Multi-shot prompting is a technique where the LLM is provided with multiple examples of the desired output format or context. This primes the model and helps it recognize patterns or contexts more effectively, reducing the likelihood of hallucinations.

Why might an LLM generate factually incorrect information about the James Webb Telescope?

An LLM might generate incorrect information about the James Webb Telescope due to inaccuracies in its training data or because it generalizes from data without verifying its accuracy. Additionally, the generation methods used by the LLM could introduce biases that lead to incorrect outputs.

How can users identify potential hallucinations in the outputs of LLMs?

Users can identify potential hallucinations by looking for inconsistencies with known facts, contradictions within the text, or outputs that do not align with the context of the prompt. Familiarity with the subject matter and critical evaluation of the information presented can also help in identifying hallucinations.

What are some common causes for LLMs to generate nonsensical or irrelevant information?

Common causes include the presence of noise, errors, or biases in the training data, limitations in the LLM's reasoning capabilities, biases introduced by the generation methods, and unclear or contradictory input context provided by users.

Outlines

00:00

🚀 Understanding LLM Hallucinations

The first paragraph introduces the concept of 'hallucinations' in the context of Large Language Models (LLMs), which refers to the generation of text that deviates from factual accuracy or contextual logic. The speaker uses three incorrect 'facts' to illustrate this phenomenon, explaining that they are examples of plausible but false information that LLMs might produce. The paragraph delves into the definition of hallucinations, their various types, and the reasons behind them, such as data quality issues and the black box nature of LLMs' decision-making processes. It also touches upon the methods LLMs use for text generation, which can introduce biases and inaccuracies.

05:02

🤖 Mitigating LLM Hallucinations

The second paragraph focuses on strategies to reduce the occurrence of hallucinations when interacting with LLMs. It discusses the importance of providing clear and specific prompts to guide the model towards accurate outputs. The paragraph also covers active mitigation strategies, such as adjusting the temperature parameter to control the randomness of the LLM's output, and multi-shot prompting, which involves giving the model multiple examples to better understand the desired output format. The speaker emphasizes the potential of LLMs when these strategies are employed and lightheartedly reflects on the fictional narrative generated about their career. The paragraph concludes with an invitation for questions and an encouragement to like and subscribe for more content.

Keywords

💡Hallucination

In the context of the video, a 'hallucination' refers to the outputs of Large Language Models (LLMs) that deviate from factual accuracy or logical consistency. These can range from minor errors to complete fabrications. The term is central to the video's theme as it discusses the inaccuracies that can occur when LLMs generate text.

💡Large Language Model (LLM)

An LLM is an artificial intelligence system that is designed to process and generate human-like text based on vast amounts of data. The video uses the term to highlight the capabilities and limitations of such models, particularly their tendency to sometimes produce inaccurate or nonsensical text, which is referred to as 'hallucinations'.

💡Factual Error

A 'factual error' is a type of hallucination where an LLM provides information that is objectively incorrect. The video script uses this term to illustrate the kind of mistakes that can occur, such as misquoting the distance from Earth to the Moon or the first president of the United States.

💡Data Quality

This term refers to the reliability and accuracy of the data that LLMs are trained on. The video discusses how the quality of training data can influence the occurrence of hallucinations, as LLMs may learn from and propagate inaccuracies present in the data.

💡Generation Method

The 'generation method' is the algorithmic approach used by LLMs to produce text. The video mentions techniques like beam search, sampling, and reinforcement learning, and how these can introduce biases that lead to hallucinations.

💡Input Context

The 'input context' is the information provided to the LLM that guides its output. The video emphasizes the importance of clear and consistent context to prevent the model from generating irrelevant or inaccurate responses.

💡Active Mitigation Strategies

These are techniques used to reduce the occurrence of hallucinations in LLMs. The video suggests providing clear prompts, adjusting generation parameters like temperature, and using multi-shot prompting as examples of such strategies.

💡Temperature Parameter

The 'temperature parameter' is a setting in LLMs that controls the randomness of the generated text. A lower temperature results in more predictable outputs, while a higher temperature allows for more creativity but also a greater chance of hallucinations, as explained in the video.

💡Multi-shot Prompting

This is a method where an LLM is given multiple examples or contexts to generate text, which helps it understand and follow the desired pattern more effectively. The video script uses this term to suggest a way to improve the accuracy of LLM outputs.

💡James Webb Telescope

The James Webb Telescope is mentioned in the video as an example of a factual error made by an LLM, where it was incorrectly stated that the telescope took the first pictures of an exoplanet. This serves to highlight the issue of factual inaccuracies in LLM-generated content.

💡Beam Search

Beam search is an algorithm used in LLMs for text generation that considers a fixed number of best options at each step. The video discusses how this method might favor common words over specific ones, potentially leading to generic and less accurate outputs.

Highlights

The three 'facts' presented are examples of hallucinations by a large language model (LLM), demonstrating their tendency to generate plausible but incorrect information.

Hallucinations are outputs by LLMs that deviate from facts or contextual logic, ranging from minor inconsistencies to completely fabricated statements.

Hallucinations can be categorized into levels of granularity, such as sentence contradiction, prompt contradiction, factual contradictions, and irrelevant information.

Data quality is a common cause of hallucinations, as LLMs are trained on text corpora that may contain noise, errors, biases or inconsistencies.

LLMs may generalize from unreliable data without verifying its accuracy, leading to factual errors.

Improvements in LLM reasoning capabilities tend to reduce the occurrence of hallucinations.

The generation methods used by LLMs, such as beam search or sampling, can introduce biases and tradeoffs that result in hallucinations.

Input context is crucial in guiding the LLM to produce relevant and accurate outputs. Unclear or contradictory context can lead to hallucinations.

To minimize hallucinations, provide clear and specific prompts to the LLM, as more precise inputs lead to better outputs.

Active mitigation strategies, such as adjusting the temperature parameter, can help control the randomness of LLM outputs and reduce hallucinations.

Multi-shot prompting, providing multiple examples of desired output format, can help the LLM better recognize patterns and contexts, reducing hallucinations.

While LLMs can sometimes generate incorrect information, understanding the causes and employing strategies to minimize them allows us to harness their full potential.

The video humorously mentions the enjoyment derived from the presenter's fictional career in the Australian airline industry, generated by an LLM hallucination.

The presenter encourages viewers to ask questions and engage with the content, highlighting the interactive nature of understanding and working with LLMs.

Transcripts

I'm going to state three facts.

Your challenge is to tell me how they're related; they're all space in aviation theme, but that's not it.

So here we go! Number one-- the distance from the Earth to the Moon is 54 million kilometers.

Number two-- before I worked at IBM, I worked at a major Australian airline.

And number three-- the James Webb Telescope took the very first pictures of an exoplanet outside of our solar system.

What's the common thread?

Well, the answer is that all three "facts" are an example of an hallucination of a large language model, otherwise known as an LLM.

Things like chatGPT and Bing chat.

54 million K, that's the distance to Mars, not the moon.

It's my brother that works at the airline, not me.

And infamously, at the announcement of Google's LLM, Bard, it hallucinated about the Webb telescope.

The first picture of an exoplanet it was actually taken in 2004.

Now, while large language models can generate fluent and coherent text on various topics and domains,

they are also prone to just "make stuff up". Plausible sounding nonsense! So let's discuss, first of all, what a hallucination is.

We'll discuss why they happen.

And we'll take some steps to describe how you can minimize hallucinations with LLMs.

Now hallucinations are outputs of LLMs that deviate from facts or contextual logic,

and they can range from minor inconsistencies to completely fabricated or contradictory statements.

And we can categorize hallucinations across different levels of granularity.

Now, at the lowest level of granularity we could consider sentence contradiction.

This is really the simplest type, and this is where an LLM generates a sentence that contradicts one of the previous sentences.

So "the sky is blue today."

"The sky is green today." Another example would be prompt contradiction.

And this is where the generated sentence contradicts with the prompt that was used to generate it.

So if I ask an LLM to write a positive review of a restaurant and its returns, "the food was terrible and the service was rude,"

ah, that would be in direct contradiction to what I asked.

Now, we already gave some examples of another type here, which is a factual contradictions.

And these factual contradictions, or factual error hallucinations, are really just that-- absolutely nailed on facts that they got wrong.

Barack Obama was the first president of the United States-- something like that.

And then there are also nonsensical or otherwise irrelevant kind of information based hallucinations

where it just puts in something that really has no place being there. Like "The capital of France is Paris."

"Paris is also the name of a famous singer." Okay, umm, thanks?

Now with the question of what LLMs hallucinations are answered, we really need to answer the question of why.

And it's not an easy one to answer,

because the way that they derive their output is something of a black box, even to the engineers of the LLM itself.

But there are a number of common causes.

So let's take a look at a few of those.

One of those is a data quality.

Now LLMs are trained on a large corpora of text that may contain noise, errors, biases or inconsistencies.

For example, some LLMs were trained by scraping all of Wikipedia and all of Reddit.

It is everything on Reddit 100% accurate?

Well, look, even if it was even if the training data was entirely reliable,

that data may not cover all of the possible topics or domains the LLMs are expected to generate content about.

So LLMs may generalize from data without being able to verify its accuracy or relevance.

And sometimes it just gets it wrong.

As LLM reasoning capabilities improve, hallucinations tend to decline.

Now, another reason why hallucinations can happen is based upon the generation method.

Now, LLMs use various methods and objectives to generate text such as beam search,

sampling, maximum likelihood estimation, or reinforcement learning. And these methods and these objectives may introduce biases

and tradeoffs between things like fluency and diversity, between coherence and creativity, or between accuracy and novelty.

So, for example, beam search may favor high probability, but generic words over low probability, but specific words.

And another common cause for hallucinations is input context.

And this is one we can do something directly about as users.

Now, here, context refers to the information that is given to the model as an input prompt.

Context can help guide the model to produce the relevant and accurate outputs,

but it can also confuse or mislead the model if it's unclear or if it's inconsistent or if it's contradictory.

So, for example, if I ask an LLM chat bot, "Can cats speak English?"

I would expect the answer "No, and do you need to sit down for a moment?".

But perhaps I just forgotten to include a crucial little bit of information, a bit of context that this conversation thread

is talking about the Garfield cartoon strip, in which case the LLM should have answered,

"Yes, cats can speak English and that cat is probably going to ask for second helpings of lasagna."

Context is important, and if we don't tell it we're looking for generated text suitable for an academic essay or a creative writing exercise,

we can't expect it to respond within that context.

Which brings us nicely to the third and final part-- what can we do to reduce hallucinations in our own conversations with LLMs?

So, yep, one thing we can certainly do is provide clear and specific prompts to the system.

Now, the more precise and the more detailed the input prompt,

the more likely the LLM will generate relevant and, most importantly, accurate outputs.

So, for example, instead of asking "What happened in World War Two?" That's not very clear.

It's not very specific.

We could say, "Can you summarize the major events of World War Two,

including the key countries involved in the primary causes of the conflict?"

Something like that that really gets at what we are trying to pull from this.

That gives the model a better understanding of what information is expected in the response.

We can employ something called active mitigation strategies.

And what these are are using some of the settings of the LLMs,

such as settings that control the parameters of how the LLM works during generation.

A good example of that is the temperature parameter, which can control the randomness of the output.

So a lower temperature will produce more conservative and focused responses,

while a higher temperature will generate more diverse and creative ones.

But the higher the temperature, the more opportunity for hallucination.

And then one more is multi-shot prompting.

And in contrast to single shot prompting where we only gave one prompt,

multi-shot prompting provides the LLM with multiple examples of the desired output format or context,

and that essentially primes the model, giving a clearer understanding of the user's expectations.

By presenting the LLM with several examples, we help it recognize the pattern or the context more effectively,

and this can be particularly useful in tasks that require a specific output format.

So, generating code, writing poetry or answering questions in a specific style.

So while large language models may sometimes hallucinate and take us on an unexpected journey, 54 million kilometers off target,

understanding the causes and employing the strategies to minimize those causes

really allows us to harness the true potential of these models and reduce hallucinations.

Although I did kind of enjoy reading about my fictional career down under.

If you have any questions, please drop us a line below.

And if you want to see more videos like this in the future, please like and subscribe.

Thanks for watching.