content.raw: 🔍 Today's pick in Interpretability & Analysis of LMs: INSIDE: LLMs' Internal States Retain the Power of Hallucination Detection by 
content.type: text
content.value: 🔍 Today's pick in Interpretability & Analysis of LMs: INSIDE: LLMs' Internal States Retain the Power of Hallucination Detection by 
content.raw: @chaochen
content.type: mention
content.user: chaochen
content.raw:  et al.
content.type: text
content.value:  et al.
content.type: new_line
content.type: new_line
content.raw: Previous efforts in detecting hallucinations using model intrinsic information employed predictive uncertainty or self-consistency to detect evaluation. Authors contend that in these procedure the rich semantic information captured in model embeddings is inevitably lost while decoding tokens.
content.type: text
content.value: Previous efforts in detecting hallucinations using model intrinsic information employed predictive uncertainty or self-consistency to detect evaluation. Authors contend that in these procedure the rich semantic information captured in model embeddings is inevitably lost while decoding tokens.
content.type: new_line
content.type: new_line
content.raw: To prevent this information loss they propose EigenScore, an internal measure of responses’ self-consistency using the eigenvalues of sampled responses' covariance matrix in intermediate model layers to quantify answers’ diversity in the dense embedding space.  Results show that EigenScore outperforms logit-level methods for hallucination detection on QA tasks, especially when paired with inference time feature clipping to truncate extreme activations, reducing overconfident generations.
content.type: text
content.value: To prevent this information loss they propose EigenScore, an internal measure of responses’ self-consistency using the eigenvalues of sampled responses' covariance matrix in intermediate model layers to quantify answers’ diversity in the dense embedding space.  Results show that EigenScore outperforms logit-level methods for hallucination detection on QA tasks, especially when paired with inference time feature clipping to truncate extreme activations, reducing overconfident generations.
content.type: new_line
content.type: new_line
content.raw: 📄 Paper: 
content.type: text
content.value: 📄 Paper: 
content.label: INSIDE: LLMs' Internal States Retain the Power of Hallucination
    Detection (2402.03744)
content.raw: https://huggingface.co/papers/2402.03744
content.id: 2402.03744
content.type: paper
content.type: resource
content.url: https://huggingface.co/papers/2402.03744
rawContent: 🔍 Today's pick in Interpretability & Analysis of LMs: INSIDE: LLMs' Internal States Retain the Power of Hallucination Detection by @chaochen et al.
  
  Previous efforts in detecting hallucinations using model intrinsic information employed predictive uncertainty or self-consistency to detect evaluation. Authors contend that in these procedure the rich semantic information captured in model embeddings is inevitably lost while decoding tokens.
  
  To prevent this information loss they propose EigenScore, an internal measure of responses’ self-consistency using the eigenvalues of sampled responses' covariance matrix in intermediate model layers to quantify answers’ diversity in the dense embedding space.  Results show that EigenScore outperforms logit-level methods for hallucination detection on QA tasks, especially when paired with inference time feature clipping to truncate extreme activations, reducing overconfident generations.
  
  📄 Paper: https://huggingface.co/papers/2402.03744
reactions.reaction: 👍
reactions.users: chaochen victor osanseviero
reactions.reaction: ❤️
reactions.users: chaochen santiviquez samusenps
commentators._id: 5e7749883d77a72421292d07
commentators.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/5e7749883d77a72421292d07/M4AmBReZk_otxCIG3o0bL.jpeg
commentators.fullname: Gabriele Sarti
commentators.name: gsarti
commentators.type: user
commentators._id: 61898e700f17069e911ba061
commentators.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/61898e700f17069e911ba061/gJ0F4nbiqXGoqIb16RwRY.jpeg
commentators.fullname: Deema
commentators.name: Deema
commentators.type: user