Large Language Models (LLMs), using deep learning techniques, perform various NLP and NLG tasks. Recent research by Microsoft and Columbia University focuses on detecting hallucination in language models, introducing probes and a dataset for efficient detection, while exploring factors affecting probe accuracy. The study contributes three probe architectures and a dataset of tagged utterances.
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Large Language Models (LLMs) in AI
Large Language Models (LLMs) are the latest innovation in Artificial Intelligence (AI) that use deep learning techniques to produce human-like text and perform various Natural Language Processing (NLP) and Natural Language Generation (NLG) tasks. These models are trained on large amounts of textual data and can perform tasks such as generating meaningful responses to questions, text summarization, translations, text-to-text transformation, and code completion.
Hallucination Detection in Language Models
A recent research team has focused on detecting hallucinations in language models, especially decoder-only transformer models. Hallucination detection aims to determine whether the generated text is true to the input prompt or contains false information.
Probes for Hallucination Detection
The research team has addressed the construction of probes to anticipate a transformer language model’s hallucinatory behavior during in-context creation tasks. Probes are trained on the model’s internal operations to predict when the model might provide delusional material. The team has emphasized the importance of providing a dataset containing examples of synthetic and biological hallucinations for training and assessing these probes.
Key Findings and Contributions
The research has shown that probes designed to identify force-decoded states of artificial hallucinations may not effectively identify biological hallucinations. The team has also explored the elements that affect the accuracy of the probe, such as the nature of the hallucinations, the size of the model, and the particular encoding components being probed. The primary contributions of the research include the production of a dataset tagged for hallucinations and the presentation of three probe architectures for efficient detection of hallucinations.
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