Summary of Can Active Label Correction Improve Llm-based Modular Ai Systems?, by Karan Taneja and Ashok Goel
Can Active Label Correction Improve LLM-based Modular AI Systems?
by Karan Taneja, Ashok Goel
First submitted to arxiv on: 10 Jan 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 The paper proposes a novel method called Active Label Correction (ALC) to improve modular AI systems by training smaller task-specific models that can replace Large Language Model (LLM)-based modules. The authors hypothesize that ALC can be applied on data traces collected from LLM deployments to minimize deployment time for complex tasks. They study the noise in three GPT-3.5-annotated datasets and their denoising with human feedback, proposing a novel method ALC3 that iteratively applies three updates: auto-correction, correction using human feedback, and filtering. The results show that ALC3 can achieve oracle performance with feedback on 17-24% fewer examples than the number of noisy examples in the dataset across three different NLP tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making AI systems better by training smaller models to replace big language models. They want to figure out how to use data from when these AI systems were used before to make them work better faster. The data has problems because it was labeled by a big language model, which isn’t perfect. They came up with a new way to fix the data called Active Label Correction (ALC). It makes three changes: first, it corrects mistakes on its own, then it uses human help to make corrections, and finally it gets rid of bad data. This new method works really well, and they showed that it can achieve great results with less data than before. |
Keywords
* Artificial intelligence * Gpt * Language model * Large language model * Nlp
