Summary of Birdie: Advancing State Space Models with Reward-driven Objectives and Curricula, by Sam Blouir et al.
Birdie: Advancing State Space Models with Reward-Driven Objectives and Curricula
by Sam Blouir, Jimmy T.H. Smith, Antonios Anastasopoulos, Amarda Shehu
First submitted to arxiv on: 1 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel training procedure, Birdie, to enhance the in-context retrieval capabilities of efficient state space models (SSMs) without altering their architecture. SSMs like linear recurrent neural networks and linear attention variants offer computational advantages but struggle with tasks requiring long-range text copying, associative recall, and question answering over long contexts. The proposed approach combines bidirectional input processing with dynamic mixtures of specialized pre-training objectives, optimized via reinforcement learning. Experimental evaluations demonstrate that Birdie improves performance on retrieval-intensive tasks like multi-number phone book lookup, long paragraph question-answering, and infilling, narrowing the gap with Transformers while retaining computational efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to make computers better at understanding and copying text from a long time ago. They want to find a way for computers to remember what they read earlier and use it to answer questions or fill in missing information. The problem is that some computer models are fast but not good at remembering things. The new method, called Birdie, helps these models get better at recalling information without making them slower. They tested Birdie on different tasks like finding phone numbers or answering long questions and found that it worked well. |
Keywords
» Artificial intelligence » Attention » Question answering » Recall » Reinforcement learning
