Summary of Bita: Bi-directional Tuning For Lossless Acceleration in Large Language Models, by Feng Lin et al.
BiTA: Bi-Directional Tuning for Lossless Acceleration in Large Language Models
by Feng Lin, Hanling Yi, Hongbin Li, Yifan Yang, Xiaotian Yu, Guangming Lu, Rong Xiao
First submitted to arxiv on: 23 Jan 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 presents Bi-directional Tuning for lossless Acceleration (BiTA), a novel method to improve the inference efficiency of large language models (LLMs). LLMs typically employ autoregressive generation during inference, which requires high memory bandwidth and results in prolonged latency. To address this inefficiency, BiTA streamlines semi-autoregressive generation and draft verification using efficient tree-based decoding. The proposed method generates draft candidates and verifies them in parallel, ensuring outputs identical to those produced by autoregressive models under greedy sampling. BiTA serves as a lightweight plug-in module that can be seamlessly integrated with existing LLMs without requiring additional assistance models or significant memory costs. The paper demonstrates the effectiveness of BiTA using the MT-Bench benchmark, achieving a 2.7x speedup with LLaMA-2-70B-Chat. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary BiTA is a new way to make large language models faster and more efficient. Right now, these models take up a lot of memory and processing power when they’re generating text. BiTA helps solve this problem by making the model generate text in a different way that’s faster and uses less memory. This is done using a special kind of decoding called tree-based decoding. The model generates multiple versions of the text at the same time and then picks the best one. This makes it much faster than before and can help with tasks like language translation and chatbots. |
Keywords
* Artificial intelligence * Autoregressive * Inference * Llama * Translation
