Podcast
Questions and Answers
Which of the following best describes a hallucination in the context of Large Language Models (LLMs)?
Which of the following best describes a hallucination in the context of Large Language Models (LLMs)?
- A feature designed to enhance the creativity of the LLM.
- An intentional misrepresentation of facts to mislead the user.
- A method of encrypting sensitive information within the LLM's output.
- An output that deviates from facts or contextual logic. (correct)
Sentence contradiction, prompt contradiction, and factual contradiction represent different levels of granularity for categorizing LLM hallucinations.
Sentence contradiction, prompt contradiction, and factual contradiction represent different levels of granularity for categorizing LLM hallucinations.
True (A)
Name two potential causes of hallucinations in LLMs.
Name two potential causes of hallucinations in LLMs.
Data quality;Generation method
The temperature parameter in LLMs controls the ______ of the output.
The temperature parameter in LLMs controls the ______ of the output.
Match the hallucination type with its description
Match the hallucination type with its description
Which factor contributes to LLM hallucinations due to imperfections in the training data?
Which factor contributes to LLM hallucinations due to imperfections in the training data?
Beam search, which is used by LLMs, always favors specific and less generic words over high probability ones.
Beam search, which is used by LLMs, always favors specific and less generic words over high probability ones.
What is one strategy to mitigate hallucinations through input context?
What is one strategy to mitigate hallucinations through input context?
Using a ______ temperature setting will produce more conservative and focused responses from an LLM.
Using a ______ temperature setting will produce more conservative and focused responses from an LLM.
What is the primary goal of using 'multi-shot prompting' when interacting with LLMs?
What is the primary goal of using 'multi-shot prompting' when interacting with LLMs?
Providing more general and ambiguous prompts tends to decrease the likelihood of LLM hallucinations.
Providing more general and ambiguous prompts tends to decrease the likelihood of LLM hallucinations.
Besides temperature, what is another setting of an LLM system that can be adjusted to actively mitigate hallucinations?
Besides temperature, what is another setting of an LLM system that can be adjusted to actively mitigate hallucinations?
LLMs may ______ information if the training data does not cover all potential topics.
LLMs may ______ information if the training data does not cover all potential topics.
In contexts where a specific output format is crucial, which prompting technique is most effective in guiding the LLM?
In contexts where a specific output format is crucial, which prompting technique is most effective in guiding the LLM?
According to research, as LLM reasoning capabilities improve, hallucinations tend to increase due to the models becoming more creative.
According to research, as LLM reasoning capabilities improve, hallucinations tend to increase due to the models becoming more creative.
Name one negative implication of training LLMs on data scraped from sources like Reddit or Wikipedia.
Name one negative implication of training LLMs on data scraped from sources like Reddit or Wikipedia.
An LLM chatbot is asked, "Can cats speak English?" To ensure an accurate response, one must provide additional ______.
An LLM chatbot is asked, "Can cats speak English?" To ensure an accurate response, one must provide additional ______.
Which of the following describes the likely outcome of setting a high temperature parameter in an LLM?
Which of the following describes the likely outcome of setting a high temperature parameter in an LLM?
The primary benefit of active mitigation strategies when using LLMs is that they completely eliminate the possibility of hallucinations.
The primary benefit of active mitigation strategies when using LLMs is that they completely eliminate the possibility of hallucinations.
Why is it difficult to precisely pinpoint the causes of hallucinations in LLMs?
Why is it difficult to precisely pinpoint the causes of hallucinations in LLMs?
Flashcards
LLM Hallucinations
LLM Hallucinations
Outputs from LLMs that deviate from facts or contextual logic.
Sentence Contradiction
Sentence Contradiction
When a generated sentence contradicts a previous sentence.
Prompt Contradiction
Prompt Contradiction
When a generated sentence contradicts the prompt.
Factual Contradictions
Factual Contradictions
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Nonsensical Information Insertion
Nonsensical Information Insertion
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Data Quality Issues
Data Quality Issues
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Generation Method Limitations
Generation Method Limitations
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Input Context Importance
Input Context Importance
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Importance of Clear Prompts
Importance of Clear Prompts
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Active Mitigation Strategies
Active Mitigation Strategies
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Multi-Shot Prompting
Multi-Shot Prompting
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Study Notes
- Large Language Models (LLMs) such as ChatGPT are prone to "hallucinations," where they generate incorrect or fabricated information.
What are Hallucinations?
- Hallucinations are LLM outputs that deviate from facts or contextual logic.
- They range from minor inconsistencies to completely fabricated statements.
Types of Hallucinations:
- Sentence Contradiction: An LLM generates a sentence that contradicts a previous one.
- Example: "The sky is blue today. The sky is green today."
- Prompt Contradiction: The generated sentence contradicts the prompt.
- Example: Asking for a positive restaurant review and getting a negative one.
- Factual Contradictions: LLMs get established facts wrong.
- Example: "Barack Obama was the first president of the United States."
- Nonsensical Information: LLMs include irrelevant or nonsensical information.
- Example: "The capital of France is Paris. Paris is also the name of a famous singer."
Why do Hallucinations Happen?
- The exact reasons are complex due to the "black box" nature of LLM operations.
- Contributing factors include data quality, generation methods, and input context.
- Data Quality: LLMs are trained on vast text corpora, which may include errors, biases, and inconsistencies. Training data may not cover all topics, causing the LLM to make inaccurate generalizations.
- Generation Method: Methods such as beam search, sampling, and reinforcement learning can introduce biases that affect accuracy.
- Input Context: Unclear, inconsistent, or contradictory prompts can confuse LLMs. Providing sufficient context is crucial for accurate responses.
Reducing Hallucinations
- Provide clear and specific prompts.
- Instead of "What happened in World War II?", ask "Can you summarize the major events of World War II, including the key countries involved and the primary causes of the conflict?"
- Use LLM settings to control output, such as the temperature parameter to control the randomness of the output.
- Lower temperatures produce more conservative responses at the cost of creativity.
- Use multi-shot prompting. Provide multiple examples to the LLM of the desired output format or context to prime the model. This is useful when a specific output format is needed (e.g., code, poetry).
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