Large language models (LLMs) have impressive capabilities in generating response but are also known for generating non-factual statements or hallucinations. Detecting hallucinations is challenging due to the lack of ground truth context. A possible solution, called SELFCHECKGPT, employs a zero-resource black-box hallucination detection method by comparing responses to the same prompt for consistency. The approach uses techniques such as BERTScore, natural language inference, and querying the LLM for verification. Experimental results show promise for this approach.
**Understanding and Mitigating LLM Hallucinations**
Large language models (LLMs) have shown impressive capabilities in generating fluent and convincing responses. However, they are prone to generating non-factual or nonsensical statements, also known as “hallucinations.” This can undermine trust in scenarios where accuracy is crucial, such as summarization and question answering.
Detecting hallucinations is challenging, both for humans and LLMs. It becomes even more difficult without access to ground truth context for consistency checks. However, one possible solution presented in a research paper called SELFCHECKGPT offers a zero-resource black-box hallucination detection method.
In this blog post, we will cover:
1. What Is LLM Hallucination
2. The Approach: SelfCheckGPT
– Consistency Check
– BERTScore
– Natural Language Inference
– LLM Prompt
3. Experiments
4. Conclusion
LLM hallucination refers to nonsensical or unfaithful generated content. For example, a user asks about Philip Hayworth, and the LLM responds with information about him being an English barrister and politician. However, there is no evidence to support this, making it a potential hallucination.
The SelfCheckGPT approach aims to detect hallucinations by comparing different samples generated by the LLM for the same prompt. In the case of Philip Hayworth, multiple samples contradict each other, indicating a potential hallucination. On the other hand, when asked about Bill Gates, the samples are consistent and can be verified easily.
The consistency check involves measuring semantic similarity between samples using metrics like BERTScore or performing natural language inference. These methods help determine if the responses are consistent with each other and decrease the likelihood of hallucinations.
In experiments, the SelfCheckGPT approach demonstrated promising results, with the LLM-Prompt method performing the best in terms of consistency. However, implementing these methods may require additional computing resources and increase latency.
To stay competitive and embrace AI, it is crucial to understand and mitigate LLM hallucinations. Automation opportunities can be identified, KPIs can be defined, and AI solutions can be implemented gradually. Tools like the AI Sales Bot from itinai.com/aisalesbot can automate customer engagement and improve sales processes.
If you want to leverage AI to transform your company, connect with us at hello@itinai.com. For more insights into AI, follow us on Telegram at t.me/itinainews or Twitter @itinaicom.
