A Survey on Hallucination in Large Language Models. In this paper, the authors examine recent efforts to detect, explain, and mitigate hallucinations in Large Language Models (LLMs). They introduce taxonomies of LLM hallucination phenomena, provide evaluation benchmarks, analyse current strategies for reducing LLM hallucination, and suggest potential avenues for future research.
I read the paper out of curiosity to understand the causes of hallucination, whether there's a remedy, and if prompting can reduce or even eliminate hallucination.
?To access the paper click here - https://arxiv.org/abs/2309.01219
Despite their impressive performance across a range of tasks, LLMs occasionally produce hallucinations. Hallucination refers to moments when LLMs generate content that:
Deviates from the user's input.
1.?????? Conflicts with previously generated content.
2.?????? Is inconsistent with accepted world knowledge.
3.?????? These hallucinations challenge the dependability of LLMs in practical applications.
Some insights into LLM hallucination include:
- Training Data Distribution: The likelihood of LLM hallucination correlates with the training data's distribution. If biases or errors are present in the training data, the model may reproduce or even exaggerate these errors.
- Problems with Content Created by People: Hallucinations can also originate from human-generated datasets, manifesting as outdated information, biases, or even made-up statements. Since LLMs learn from these datasets, they can adopt and replicate these hallucinations.
- Knowledge Memorization Abilities: This refers to an LLM's ability to remember, retrieve, and reason with the vast amount of information it's trained on. Shortcomings in these abilities can result in hallucinations.
- Overestimation of Abilities: LLMs can sometimes overestimate their capabilities. They may be able to assess the accuracy of their answers but might not always discern the limits of their knowledge.
Strategies to Reduce Hallucinations in LLMs
Mitigation during Pre-training:
- Knowledge Acquisition from Credible Sources: Gathering accurate and current knowledge from trustworthy sources can "hot patch" LLMs, updating their knowledge and correcting any outdated or false information.
- External Knowledge Bases: Data can be sourced from external knowledge repositories, such as vast unstructured datasets, structured databases, specific sites like Wikipedia, or the entire web.
Optimizing Model Architecture:
- Multi-branch Decoder (MBD): This technique uses a multi-branch decoder to potentially diminish hallucinations. What is a MBD? ?Imagine you're trying to solve a puzzle, and there are multiple ways to approach it. Instead of just following one way, you decide to explore several paths at the same time to see which one works best. A MBD does something similar for language models. It allows the model to generate multiple possible answers or outputs simultaneously. By exploring these different "branches" or paths, the model can then choose the best or most accurate response. This approach can help in reducing mistakes or inaccuracies, like hallucinations, in the model's outputs.
- Uncertainty-aware Decoder: This method emphasizes recognizing the model's prediction uncertainties to counteract hallucinations.
- Bidirectional Autoregressive Architecture: This strategy models language both forwards and backwards. Using bidirectional data might help decrease hallucinations.
- Generation-time Supplement: This method involves appending retrieved knowledge or feedback to user queries before prompting the LLM. It capitalizes on the LLM's in-context learning ability, allowing the model to glean and use crucial information from the context to correct false claims.
- Post-hoc Correction: This strategy employs an auxiliary "fixer" to amend hallucinations during post-processing. After the LLM produces an initial answer, this fixer inspects and rectifies any errors or hallucinations.
How Prompting Can Minimize or Eradicate Hallucinations:
Chain-of-Thought Prompt: This technique requires LLMs to produce reasoning steps before giving the final response. By compelling the model to think through its answer sequentially, it might lower the likelihood of hallucination. However, this approach could also lead to hallucinated reasoning steps.
The key insight is that using accurate and factually correct data can greatly diminish hallucinations in LLMs. Although the paper offers qualitative observations and cites multiple studies, it doesn't present specific numerical data or statistics that directly correlate the precision of training data with a decrease in hallucinations. Nonetheless, the cited studies and references bolster the idea that accurate data is crucial in mitigating hallucinations in LLMs.
#llm #llms #hallucination #chatgpt
