Summary of Teaching Smaller Language Models to Generalise to Unseen Compositional Questions (full Thesis), by Tim Hartill
Teaching Smaller Language Models To Generalise To Unseen Compositional Questions (Full Thesis)
by Tim Hartill
First submitted to arxiv on: 25 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed research investigates how Pretrained large Language Models (LLMs) can be applied to various reasoning systems despite their limited training data. The study highlights that while LLMs can answer novel questions, different applications require distinct approaches considering factors like latency, cost, compute resources, and internet connectivity. Specifically, the researchers focus on a scenario where local compute capacity is available at inference time but internet connectivity is not. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how large Language Models (LLMs) can be used for various reasoning tasks despite their limitations. By understanding what LLMs can do and why they matter, we can develop new applications that consider factors like latency, cost, and resource availability. In this study, the researchers look at a specific scenario where local compute power is available but internet connection is not. |
Keywords
» Artificial intelligence » Inference
