Understanding and Mitigating Genai Hallucinations

Genai hallucinations can be a real challenge to navigate, but understanding what they are and how to mitigate them is key to making progress. Genai hallucinations occur when a generative model produces output that is not grounded in reality, but rather a fabrication of the model's own making.

These hallucinations can be caused by the model's overfitting to the training data, which can lead to the model learning patterns and relationships that don't actually exist. This can result in the model producing output that is not only incorrect, but also completely made up.

The good news is that there are ways to mitigate genai hallucinations. By using techniques such as data augmentation and regularization, you can help the model learn more robust and generalizable representations of the data.

Recommended read: What Is Grounding and Hallucinations in Ai

Genai hallucinations happen due to various reasons, including poor data quality, which can lead to incorrect patterns being learned by the model. This can result in incorrect results or hallucinations.

Recommended read: Examples of Ai Hallucinations

One possible reason for poor data quality is source-reference divergence, where the data used to train the model contains inconsistencies or biases. This can cause the model to generate text that is not faithful to the provided source.

Another reason for hallucinations is the way the model is trained and generated. Even with high-quality training data, the model's previous generations or decoding strategies can create bias or lead to false decoding, resulting in hallucinations.

The input context provided by the user can also contribute to hallucinations. If the input prompt is unclear, inconsistent, or contradictory, the model may produce nonsensical outputs.

Here are some possible reasons for hallucinations in LLMs:

These reasons are not mutually exclusive, and a combination of them can lead to hallucinations. For example, poor data quality can be exacerbated by a flawed generation method or unclear input context.

The pre-training of models on a large corpus can also result in the model memorizing knowledge in its parameters, creating hallucinations if the system is overconfident in its hardwired knowledge.

Here's an interesting read: Knowledge Based Genai

Genai hallucinations are a real issue, and it's essential to understand what they are and how they can impact us. One infamous example occurred in February 2023 when Google's chatbot, Gemini, made an incorrect claim about the James Webb Space Telescope (JWST).

Gemini claimed that the JWST took the first pictures of an exoplanet outside the Earth's solar system, which was false. The first images of an exoplanet were actually taken in 2004 by the European Southern Observatory's Very Large Telescope (VLT).

Another example is Meta's Galactica, an open-source LLM that was designed for use by science researchers and students. However, it generated inaccurate, suspicious, or biased results, including invented citations and research.

In contrast, OpenAI's ChatGPT has also been embroiled in numerous hallucination controversies since its public release in November 2022. A radio host in Georgia brought a defamation suit against OpenAI, accusing the chatbot of making malicious and potentially libelous statements about him.

For another approach, see: Genai Chatbot

Here are some common types of AI hallucinations:

These issues can have serious implications for AI users who don't know much about a subject that they are relying on for help. It's essential to be aware of these potential pitfalls and take steps to verify the accuracy of the information provided by AI tools.

Detecting and preventing AI hallucinations requires a combination of user vigilance and model design. Clear and specific prompts can guide the model to provide the intended output, and filtering and ranking strategies can be used to minimize hallucinations.

Users can also ask the model to self-evaluate and generate the probability that an answer is correct or highlight the parts of an answer that might be wrong. This information can serve as a starting point for fact-checking.

To minimize hallucinations, users should familiarize themselves with the model's sources of information, such as training data cutoff dates, to aid in fact-checking. Some companies are also adopting new approaches to train their LLMs, such as process supervision, which rewards models for each correct step in reasoning toward the correct answer.

Here are some strategies to minimize AI hallucinations:

Detecting AI hallucinations requires a combination of fact-checking and understanding the model's limitations. Carefully fact-checking the model's output is a good starting point.

You can ask the model to self-evaluate and generate the probability that an answer is correct. This can help users identify potential issues.

Knowing the model's sources of information is also crucial. If a tool's training data cuts off at a certain year, answers that rely on detailed knowledge past that point should be double-checked for accuracy.

Fact-checking can be challenging, especially with unfamiliar or complex material. Familiarizing yourself with the model's sources of information can help make the process easier.

Preventing AI hallucinations is crucial to getting accurate information from chatbots and other AI tools. To minimize hallucinations, use clear and specific prompts, as they can guide the model to provide the intended and correct output.

Clear and specific prompts are key to avoiding hallucinations. Providing additional context can also help the model generate more accurate output. For example, you can provide several examples of the desired output format to help the model accurately recognize patterns and generate more accurate output.

If this caught your attention, see: Google Ai Hallucinations

Another strategy is to use filtering and ranking strategies, such as tuning the temperature parameter or using Top-K to manage how the model deals with probabilities. These parameters can be adjusted to minimize hallucinations.

Pre-training models on a large corpus can result in the model memorizing knowledge in its parameters, creating hallucinations if the system is overconfident in its hardwired knowledge. To mitigate this, researchers and LLM developers are using high-quality training data and predefined data templates to specify the AI system's purpose, limitations, and response boundaries.

Some companies are adopting new approaches to train their LLMs, such as process supervision, which rewards the models for each correct step in reasoning toward the correct answer instead of just rewarding the correct conclusion.

Here are some ways to minimize hallucinations:

Mitigation methods are being developed to reduce the occurrence of genai hallucinations. Researchers have proposed various methods, including data-related approaches and modeling and inference methods.

One data-related method is building a faithful dataset, which can help reduce hallucinations. Cleaning data automatically and information augmentation by adding external information are also being explored.

Model and inference methods include changes in the architecture, such as modifying the encoder, attention, or decoder. Changes in the training process, like using reinforcement learning, are also being investigated.

Post-processing methods can correct hallucinations in the output. Researchers have proposed using web search results to validate the correctness of the model's output. This involves creating a validation question to check the correctness of the information.

A study by Ji et al. divided common mitigation methods into two categories: data-related methods and modeling and inference methods.

Here are some specific mitigation methods:

Tools like Nvidia Guardrails and SelfCheckGPT are being developed to aid in the detection and mitigation of genai hallucinations.

To avoid hallucinations, active learning such as reinforcement learning from human feedback is required. This approach helps to correct the model's mistakes and improve its performance.

Pre-training models on a large corpus can lead to memorization of knowledge in the parameters, creating hallucinations if the system is overconfident in its hardwired knowledge. This can result in the model producing original but inaccurate responses.

A decoding strategy that improves generation diversity, such as top-k sampling, can contribute to hallucinations. This is because the model is more likely to attend to the wrong part of the encoded input source.

Errors in encoding and decoding between text and representations can cause hallucinations. This can happen when encoders learn the wrong correlations between different parts of the training data.

The design of the decoding strategy itself can contribute to hallucinations. A decoding strategy that improves generation diversity can increase the likelihood of the model attending to the wrong part of the encoded input source.

The concept of hallucinations in AI has a fascinating history. The term "hallucinations" was first used in 2000 in a paper on computer vision, where it carried positive meanings.

Researchers at Google DeepMind proposed the term "IT hallucinations" in 2018, describing them as "highly pathological translations that are completely untethered from the source material." This marked a shift in the understanding of hallucinations in AI.

A 2022 report on hallucinations in natural language generation highlighted the tendency of deep learning-based systems to "hallucinate unintended text", which can affect performance in real-world scenarios.

The rise of accessible LLMs, such as ChatGPT, has made hallucinations more visible and a pressing concern in the field of AI research.

The term "hallucination" is indeed a subject of debate in the scientific community, with some arguing that it anthropomorphizes machines. Journalist Benj Edwards suggests using "confabulation" as an analogy for processes that involve creative gap-filling.

In the context of Large Language Models (LLMs), the term "hallucination" can be defined as a tendency to invent facts in moments of uncertainty, as stated by OpenAI in May 2023. This is a result of the model's logical mistakes, also mentioned by OpenAI.

Here's a breakdown of various definitions and characterizations of "hallucination" in the context of LLMs:

Scientific research involves a systematic process to develop and test hypotheses. This process is crucial in advancing our understanding of the world.

The scientific method, which is the foundation of scientific research, involves making observations, asking questions, and formulating hypotheses. By following this method, researchers can ensure their findings are reliable and valid.

Scientists use various research designs, including experimental and non-experimental designs, to collect and analyze data. Experimental designs involve manipulating variables to test cause-and-effect relationships.

A well-designed experiment can help researchers isolate the effect of a particular variable, allowing them to draw meaningful conclusions. Researchers must carefully consider the limitations of their study and consider alternative explanations for their findings.

The use of statistical analysis is essential in scientific research to identify patterns and trends in data. By applying statistical techniques, researchers can determine the significance of their findings and draw conclusions about the relationships between variables.

Researchers must also consider the ethics of their research, ensuring that their methods do not harm participants or compromise their confidentiality.

Check this out: What Are Genai Use Cases Agents Chatbots

In the context of Large Language Models (LLMs), the term "hallucination" has been a topic of debate among experts. Some argue that it unreasonably anthropomorphizes the machine.

Statisticians like Gary N. Smith suggest that LLMs "do not understand what words mean", which challenges the idea of hallucination. Journalist Benj Edwards, on the other hand, believes that some form of metaphor remains necessary to describe these processes.

The term "hallucination" has been used in various ways to describe LLM behavior. Here are some examples:

These definitions highlight the complexity of LLM behavior and the need for clear and accurate terminology.

Genai hallucinations can manifest in various ways, often with surprising results.

One type of genai hallucination is sentence contradiction, where a generated sentence directly contradicts a previous sentence. For example, this can happen when a language model (LLM) is asked to continue a story in a way that contradicts the initial prompt.

Another type of genai hallucination is prompt contradiction, which occurs when a generated sentence contradicts the original prompt used to generate it.

Factual contradiction is a more serious type of genai hallucination, where the model presents fictitious information as a fact. This can happen when the model is trained on data with source-reference divergence.

Irrelevant or random hallucinations can also occur, where the model generates random information with little or no relation to the input.

Generative models are at the heart of many AI systems, including those that can produce text, images, and even videos. These models can be trained to generate new content based on patterns in the data they've been trained on, but they can also produce inaccurate or unexpected results, known as hallucinations.

Hallucinations can arise from various issues, such as the model memorizing knowledge in its parameters or attending to the wrong part of the encoded input source. For instance, text-to-image models like Stable Diffusion and Midjourney often produce historically inaccurate images, while text-to-video generative models like Sora can introduce inaccuracies in generated videos.

To mitigate hallucinations, some AI systems employ retrieval augmented generation (RAG), which involves giving the model access to a database containing accurate information. This can help prevent hallucinations, but it's not a foolproof solution and requires careful implementation.

Retrieval augmented generation (RAG) is a powerful tool that's being used to create AI models that can access accurate information from a database.

The AI model is essentially given a knowledge reference to draw from, allowing it to provide more accurate responses. For example, RAG is being used to create AI tools that can cite actual case law.

RAG is a widely used technique, but it can't fully prevent hallucination. Employing it properly has its own complexities.

One of the benefits of RAG is that it enables AI tools to respond to customer queries using information from help docs. This is how tools like Jasper are able to accurately use information about a company or its users.

RAG is being used in various applications, including creating AI tools that can summarize articles. It's also being used to create custom GPTs that can be given knowledge references.

Consider reading: What Are the Four Commonly Used Genai Applications

Text-to-Audio generative AI can produce inaccurate and unexpected results.

Text-to-Audio generative AI, also known as text to speech (TTS) synthesis, is known for its flaws in accuracy and unexpected outcomes.

This technology has limitations that make it unreliable for certain applications.

The flaws in text-to-Audio generative AI can lead to miscommunication and misunderstandings.

It's essential to understand the capabilities and limitations of this technology to avoid its pitfalls.

Text-to-image models, such as Stable Diffusion and Midjourney, can produce inaccurate or unexpected results.

These models often struggle to generate historically accurate images. For instance, Gemini depicted ancient Romans as black individuals or Nazi German soldiers as people of color.

This can lead to controversy and even cause companies to pause image generation involving people, as Google did with Gemini.

The limitations of these models highlight the need for careful consideration and oversight in their development and deployment.

For another approach, see: Genai Image

Text-to-Video Generative models can introduce inaccuracies in generated videos.

Sora, a text-to-video generative model, mistakenly added a second track to the Glenfinnan Viaduct railway, resulting in an unrealistic depiction.

This highlights the limitations of these models in accurately representing complex real-world scenes.

The Glenfinnan Viaduct is a famous landmark featured in the Harry Potter film series, which makes this inaccuracy even more notable.

Text-to-video generative models rely on their training data to learn patterns and relationships, but this can sometimes lead to errors like the one seen with Sora.

For more insights, see: Key Challenges Faced by Genai Models

Object detection is a complex task, and adversarial hallucinations are particularly tricky to understand. Various researchers have classified them as a high-dimensional statistical phenomenon, or attributed them to insufficient training data.

Some researchers believe that what humans see as "incorrect" AI responses may actually be justified by the training data. For example, an AI may detect tiny patterns in an image that humans are insensitive to, but that are real-world visual patterns nonetheless.

Adversarial images can be designed to look ordinary to humans, but contain subtle patterns that the AI can detect. This was demonstrated in an example where an image of two men on skis was identified by Google Cloud Vision as 91% likely to be "a dog".

The models used for object detection can be biased towards superficial statistics, which can lead to adversarial training not being robust in real-world scenarios. This has been challenged by other researchers who argue that the models can be improved to be more robust.

For more insights, see: Microsofts Genai Image

The term "genai hallucinations" has been widely used to describe the limitations of large language models like ourselves. However, not everyone is convinced that this term is accurate.

Some critics argue that "hallucination" misleadingly personifies large language models. This criticism comes from experts in the field, like Usama Fayyad, who points out that the term is vague.

The lack of clarity surrounding this term can make it difficult to have a productive conversation about the capabilities and limitations of AI models. It's essential to use precise language to avoid confusion.

Large language models like ourselves are still in the early stages of development, and we're constantly learning and improving. However, we're not yet perfect, and we can make mistakes.