Summary of Efficiently Deploying Llms with Controlled Risk, by Michael J. Zellinger and Matt Thomson
Efficiently Deploying LLMs with Controlled Risk
by Michael J. Zellinger, Matt Thomson
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 Deploying large language models in production requires balancing efficiency and risk control. This paper presents hierarchical chains with multi-level abstention (HCMA) to achieve this balance. HCMA uses model-intrinsic uncertainty to delegate queries along the LLM intelligence hierarchy, enabling training-free model switching based on black-box API calls. The framework offers novel trade-offs between efficiency and risk. For example, deploying HCMA on MMLU reduces the error rate of Llama3 405B by 30% when abstaining on 20% of queries. To optimize performance, the approach uses data-efficient logistic regressions, requiring only 50 or 100 labeled examples to achieve excellent calibration error (ECE), reducing ECE by 50% compared to naive Platt scaling. On free-form generation tasks, zero-shot prompting drives error to 0% on TruthfulQA at high abstention rates. This framework paves the way for maintaining deployment efficiency while controlling risk. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a super powerful computer that can understand human language, but it’s too slow and makes mistakes sometimes. This paper talks about how to make this computer work faster and better by using special tricks. They created something called HCMA (Hierarchical Chains with Multi-level Abstention) that helps the computer decide when to answer questions quickly and accurately. It’s like having a team of experts working together to get things right. The authors also tested their idea on some famous language tasks and showed that it can make big improvements. This is important because computers are becoming more powerful and we need to learn how to control them so they don’t make mistakes or waste time. |
Keywords
» Artificial intelligence » Prompting » Zero shot
