Summary of Bbox-adapter: Lightweight Adapting For Black-box Large Language Models, by Haotian Sun et al.
BBox-Adapter: Lightweight Adapting for Black-Box Large Language Models
by Haotian Sun, Yuchen Zhuang, Wei Wei, Chao Zhang, Bo Dai
First submitted to arxiv on: 13 Feb 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 Adapting state-of-the-art Large Language Models (LLMs) like GPT-4 and Gemini for specific tasks is challenging due to their opacity in parameters, embeddings, and output probabilities. Existing fine-tuning adaptation methods are inapplicable, requiring API services that raise concerns about transparency, privacy, and cost. To address these challenges, the authors introduce BBox-Adapter, a novel lightweight adapter for black-box LLMs. It employs Noise Contrastive Estimation (NCE) loss to promote target domain data likelihood while penalizing source domain data. The adapter features an online adaptation mechanism incorporating real-time positive data sampling and negative data from previous adaptations. Experimental results demonstrate the effectiveness and cost efficiency of BBox-Adapter, improving model performance by up to 6.77% across diverse tasks and domains, while reducing training and inference costs by 31.30x and 1.84x, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about making language models work better for specific jobs. The problem is that these models are really good at doing general things, but they’re not very flexible or transparent. Currently, we can only use them through special services that make us worry about things like privacy and cost. To fix this, the authors created a new tool called BBox-Adapter. It helps language models learn from specific data by making it more likely to find good information and less likely to find bad information. The authors tested their tool and found that it works really well, improving performance by up to 6.77% and reducing costs by a lot. |
Keywords
* Artificial intelligence * Fine tuning * Gemini * Gpt * Inference * Likelihood
