
Reasoning Over Complex Documents with DocLLM with Armineh Nourbakhsh - #672
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Enhancing Models with Fill in the Middle Objectives
Implementing fill in the middle objectives in model training can enhance model robustness. By modifying the decoder model to function as an encoder model, tasks are designed where the model generates a middle span after masking a span in the input sequence. This approach provides the model access to contextual information while still operating in a generative setting. Advanced variations include masking multiple tokens and shuffling their order to increase task complexity while maintaining positional information. Implementing this technique in the doc LLM architecture has shown significant improvements, especially when applied to documents like forms.
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