Row 7023

Row ID: 7023 | Dataset Entry | Axioma AXP Content Repository

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What are your best guesses on how it works (training and architecture) vs. the typical VL formula of pretrained vision encoder + pretrained LLM -> fine-tune with multimodal tasks?

E.g. Is it fully mixed modality pre-training the entire system? Does model embed all modalities into a shared space for prediction? Does the system "self-select" the modality of output tokens (can flexibly choose to output audio vs. text based on input tokens) or is this user specified?

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text What are your best guesses on how it works (training and architecture) vs. the typical VL formula of pretrained vision encoder + pretrained LLM -> fine-tune with multimodal tasks? E.g. Is it fully mixed modality pre-training the entire system? Does model embed all modalities into a shared space for prediction? Does the system "self-select" the modality of output tokens (can flexibly choose to output audio vs. text based on input tokens) or is this user specified?
label r/machinelearning
dataType post
communityName r/MachineLearning
datetime 2024-05-14
username_encoded Z0FBQUFBQm5LakwzMjdQakdUUGJMOFpEY2tvcENPaTQ1bmNUZTl1aDdzdGx0UXJ0QWxfc2hUMmVIeVoyVng1Z0w3ZlMwR0g5UkY3akVWN1RtRFQxQ3V5ZTl2WGtjb2RGQUE9PQ==
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Raw Record

{
  "text": "What are your best guesses on how it works (training and architecture) vs. the typical VL formula of pretrained vision encoder + pretrained LLM -> fine-tune with multimodal tasks?\n\nE.g. Is it fully mixed modality pre-training the entire system? Does model embed all modalities into a shared space for prediction? Does the system \"self-select\" the modality of output tokens (can flexibly choose to output audio vs. text based on input tokens) or is this user specified?",
  "label": "r/machinelearning",
  "dataType": "post",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-14",
  "username_encoded": "Z0FBQUFBQm5LakwzMjdQakdUUGJMOFpEY2tvcENPaTQ1bmNUZTl1aDdzdGx0UXJ0QWxfc2hUMmVIeVoyVng1Z0w3ZlMwR0g5UkY3akVWN1RtRFQxQ3V5ZTl2WGtjb2RGQUE9PQ==",
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}
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