Beginner Explanation
Imagine you have a friend who loves to tell stories. Sometimes, they get really creative and make up things that never happened, like a dragon living in your backyard. This is similar to what we call ‘hallucination’ in AI. When an AI model generates information or answers that aren’t based on the real world or the data it was trained on, it’s like that friend inventing stories. The AI is trying to be helpful, but sometimes it just makes things up that aren’t true!Technical Explanation
Hallucination behaviors in AI models, particularly in natural language processing, refer to instances where the model produces outputs that deviate from the input data or factual reality. This can occur due to overfitting, insufficient training data, or inherent biases in the model. For example, in a text generation task using a transformer model like GPT, the model might generate a sentence that contradicts the context provided. To mitigate hallucinations, techniques such as reinforcement learning from human feedback (RLHF) and careful dataset curation are employed. Here’s a simple code snippet using Hugging Face’s Transformers library to demonstrate text generation: “`python from transformers import GPT2LMHeadModel, GPT2Tokenizer model = GPT2LMHeadModel.from_pretrained(‘gpt2’) tokenizer = GPT2Tokenizer.from_pretrained(‘gpt2’) input_text = ‘The capital of France is’ input_ids = tokenizer.encode(input_text, return_tensors=’pt’) output = model.generate(input_ids, max_length=50) print(tokenizer.decode(output[0], skip_special_tokens=True)) “` This code generates text based on the input, but without careful tuning, it may produce hallucinated content.Academic Context
Hallucination behaviors in AI models are a significant area of concern in machine learning and natural language processing. Research has shown that models can generate false or misleading information, which can have serious implications in applications such as automated content generation and conversational agents. Theoretical frameworks for understanding hallucinations often involve concepts from cognitive science and linguistics, focusing on how models interpret and generate language. Key papers include ‘Language Models are Few-Shot Learners’ by Brown et al. (2020) and ‘The Risks of Relying on Language Models’ by Bender et al. (2021), which discuss the limitations and ethical considerations of language models, including their propensity for hallucination.Code Examples
Example 1:
from transformers import GPT2LMHeadModel, GPT2Tokenizer
model = GPT2LMHeadModel.from_pretrained('gpt2')
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
input_text = 'The capital of France is'
input_ids = tokenizer.encode(input_text, return_tensors='pt')
output = model.generate(input_ids, max_length=50)
print(tokenizer.decode(output[0], skip_special_tokens=True))Example 2:
from transformers import GPT2LMHeadModel, GPT2Tokenizer
model = GPT2LMHeadModel.from_pretrained('gpt2')
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
View Source: https://arxiv.org/abs/2511.16668v1