Introduction to the LLM Hallucination Survey Project
The llm-hallucination-survey is an ambitious initiative centered around understanding and addressing the issue of "hallucination" in large language models (LLMs). This project delves deep into the reasons why LLMs occasionally generate false or misleading content and proposes ways to evaluate, explain, and mitigate these occurrences. Such hallucinations can occur when the output deviates from user input, existing context, or known facts. Given the critical role of LLMs in various applications, resolving hallucination is vital for improving their reliability.
News and Updates
Recently, a comprehensive survey titled "Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models" was published, highlighting the project's latest findings. The survey provides a detailed exploration of the hallucination problem within LLMs, covering evaluation methods, explanations, and potential solutions. Researchers and practitioners in the field are encouraged to cite this survey in their work.
Evaluation of LLM Hallucination
Input-conflicting Hallucination
This type of hallucination is observed when a model's response diverges from the initial task instructions or inputs from the user. Notable research areas include:
- Machine Translation: Studies have assessed and addressed hallucinations in neural machine translation systems.
- Data-to-text: Efforts have been made to control hallucinations at a word level when generating text from data.
- Summarization: The faithfulness and factuality of summaries have been scrutinized to minimize hallucinatory content.
- Dialogue: Strategies have been proposed to reduce hallucinations in conversational AI by grounding responses in knowledge.
- Question Answering: Research has focused on detecting entity-based knowledge conflicts and ensuring factual correctness in responses.
Context-conflicting Hallucination
This form of hallucination arises when generated content conflicts with previously created content, leading to self-contradiction. Preliminary research includes fine-tuning methods to handle unseen dialogue entities and efforts to align language generation tasks more closely with historical context.
Fact-conflicting Hallucination
In fact-conflicting scenarios, the generated content contradicts established facts, presenting a notable challenge for LLMs. Substantial research has evaluated how models mimic human falsehoods and proposed benchmarks for factuality evaluation in text generation.
Source of LLM Hallucination
The project identifies potential origins of hallucination in LLMs, such as neural network biases, data inadequacies, and model architecture limitations. Understanding the source is crucial for addressing the problem effectively.
Mitigation of LLM Hallucination
To tackle hallucination issues, the project explores numerous mitigation strategies. These include enhancing training data quality, refining model architectures, and employing external verification tools to corroborate factual content. Moreover, efforts are underway to improve LLMs' alignment with factual databases and better handle dynamic information.
Contact
For additional information or inquiries about the llm-hallucination-survey project, interested parties are encouraged to reach out to the research team through the project's official communication channels. Collaborations and feedback are warmly welcomed to help advance this critical area of AI research.
By understanding and mitigating LLM hallucinations, this project plays a crucial role in advancing the field of AI, ensuring that LLMs remain trustworthy and effective tools for countless applications.