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- Information Flow Reveals When to Trust Language Models
We use information flow to build a layer-wise trace that reveals each context token’s contribution to the output, providing an interpretable basis for assessing reliability From this analysis, we introduce two measures to calibrate prediction confidence
- INFORMATIONFLOWREVEALS WHEN TOTRUSTLANGUAGEMODELS - OpenReview
o understand how language models produce outputs In this 111 work, we propose a novel UQ method based on information flow (Ferrando et al , 2022; Ferrando 112 Voita, 2024), leveraging the model’s attention mechan
- Information Flow Routes: Automatically Interpreting Language Models at . . .
Information flows by routes inside the network via mechanisms implemented in the model These routes can be represented as graphs where nodes correspond to token representations and edges to computations
- Information Flow Routes: Automatically Interpreting Language Models at . . .
Information flows by routes inside the network via mechanisms implemented in the model These routes can be represented as graphs where nodes correspond to token representations and edges to operations inside the network
- How Transformers Think: The Information Flow That Makes Language Models . . .
This article provides a gentle and conceptual tour through the journey experienced by text-based information when it flows through the signature model architecture behind LLMs: the transformer
- SHIFT: Smoothing Hallucinations by Information Flow Tuning for . . .
In this paper, we provide a novel perspective for the causes and mitigations for halluci-nations by tracking the information flow within MLLMs We find that information in MLLMs does not flow in a strictly continuous manner, instead, they may mutate abruptly in deep layers
- Language models cannot reliably distinguish belief from . . . - Nature
As language models (LMs) increasingly infiltrate into high-stakes domains such as law, medicine, journalism and science, their ability to distinguish belief from knowledge, and fact from
- arxiv简读 2024. 11. 29--视觉信息在MLLM中究竟是如何流转的？
Cross-modal Information Flow in Multimodal Large Language Models 2024 11 29 今天非常有意思的一篇论文，探究 多模态模型 中，视觉信息在模型内部究竟是如何流转的，阿姆斯特丹大学，哥本哈根大学和慕尼黑大学出品，很有启发。
- Understanding the Information Flow inside Large Language
Moving forward, exploring various types of interventions and their effects on information flow may hold the key to gaining a deeper understanding of how language models process information
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