At Vectara, we call this concept “Grounded Generation,” but it’s also commonly known as “Retrieval Augmented Generation” (RAG) in academic literature. This has been shown in a number of studies to reduce the rates of hallucinations in LLMs (Benchmarking Large Language Models in Retrieval...
‘Hallucinations’ are a critical problem9for natural language generation systems using large language models (LLMs), such as ChatGPT1or Gemini2, because users cannot trust that any given output is correct. Hallucinations are often defined as LLMs generating “content that is nonsensical or unfai...
Researchers need a general method for detecting hallucinations in LLMs that works even with new and unseen questions to which humans might not know the answer. Here we develop new methods grounded in statistics, proposing entropy-based uncertainty estimators for LLMs to detect a subset of ...
Large language model (LLM) systems, such as ChatGPTor Gemini, can show impressive reasoning and question-answering capabilities but often 'hallucinate' false outputs and unsubstantiated answers. Answering unreliably or without the necessary information prevents adoption in diverse fields, with problems in...
Models See Hallucinations: Evaluating the Factuality in Video Captioning Hui Liu, Xiaojun Wan 2023 FactPEGASUS: Factuality-Aware Pre-training and Fine-tuning for Abstractive Summarization David Wan, Mohit Bansal 2022 Evaluating the Factual Consistency of Large Langua...
RefChecker provides a standardized assessment framework to identify subtle hallucinations present in the outputs of large language models (LLMs). Figure: RefChecker Framework 🌟 Highlighted Features Finer granularity- RefChecker breakdowns the claims in the LLM’s response intoknowledge triplets, as opp...
RefChecker provides a standardized assessment framework to identify subtle hallucinations present in the outputs of large language models (LLMs). Figure: RefChecker Framework 🌟 Highlighted Features Finer granularity- RefChecker breakdowns the claims in the LLM’s response intoknowledge triplets, as opp...
Content of command hallucinations predicts self-harm but not violence in a medium secure unit Evidence to date has supported negative relationships, a null relationship and a positive relationship between command hallucinations and violence or self-... P Rogers,A Watt,NS Gray,... - 《Journal of...
The use of GAI in scientific communication is significantly hampered by its inclination to generate false facts called hallucinations [124,125]. In the present study, the ability of the model to produce fictional content is limited by the use of predefined source texts. The LLM was asked to ...
[20]. The architecture was claimed to significantly enhance interpretability, allowing for the creation of a novel saliency map to explain any output token, the identification of model hallucinations, and the assessment of model biases through semantic adversarial perturbations. In a novel approach, ...