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Hace 5 días · Our study explores particular neurons, activation layers, and tokens that play a crucial role in the LLM perception of uncertainty and hallucination risk. By a probing estimator, we leverage LLM self-assessment, achieving an average hallucination estimation accuracy of 84.32% at run time.
Hace 1 día · Hallucination in large language models usually refers to the model generating unfaithful, fabricated, inconsistent, or nonsensical content. As a term, hallucination has been somewhat generalized to cases when the model makes mistakes. Here, I would like to narrow down the problem of hallucination to be when the model output is fabricated and not grounded by either the provided context or world ...
Hace 3 días · We introduce a hallucination detection framework for LLM-generated content. Given an existing dataset of hallucinations and true statements, we 1) leverage semantically rich sentence embeddings, 2) construct a graph structure where semantically similar sentences are connected, 3) train a Graph Attention Network (GAT) model that facilitates message passing, neighborhood attention attribution ...
Hace 5 días · In this study, we introduce a novel Relationship Hallucination Benchmark (R-Bench) designed specifically for assessing relationship hallucinations in LVLMs. This benchmark comprises image-level and instance-level questions, labeled as ’Yes’ or ’No’, similar to the POPE evaluation (Li et al., 2023e).
Hace 1 día · Vectara’s hallucination leaderboard on GitHub currently ranks GPT 4 Turbo on top with a 2.5% hallucination rate. The worst performer at the time of writing was Apple’s OpenELM-3B-Instruct, at 22.4%. Most AI models on the list generate made-up facts at rates of between 4.5 and 10%.
Hace 5 días · The AI hallucination phenomenon is equally disconcerting as it is entertaining. Hallucinations in AI can introduce potentially disastrous risks to organizations or provide a helpful muse for creatives with off-the-beaten-path fantasies.
Hace 4 días · RAGs will still induce hallucinations, leading to issues like context relevance and Q&A relevance failures. In contrast, fine-tuning, prompt engineering, and Aporia Guardrails aim to diminish hallucination likelihood by bolstering LLM performance and safety.
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