Home Community Researchers at Stanford Introduce Gisting: A Novel Technique for Efficient Prompt Compression in Language Models

Researchers at Stanford Introduce Gisting: A Novel Technique for Efficient Prompt Compression in Language Models

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Researchers at Stanford Introduce Gisting: A Novel Technique for Efficient Prompt Compression in Language Models

Model specialization involves adapting a pre-trained machine-learning model to a particular task or domain. In Language Models (LMs), model specialization is crucial in improving their performance in various tasks like summarization, question-answering, translation, and language generation. The 2 predominant processes to specialize a language model to specific tasks are instruction fine-tuning (adapting a pre-trained model to a brand new task or set of tasks) and model distillation (transferring knowledge from a pre-trained, “teacher” model to a smaller, specialized, “student” model). Prompting is a key concept in the sector of LM specialization, because it provides a strategy to guide the model towards specific behaviors, allows for more efficient use of limited training data, and is crucial for achieving state-of-the-art performance. Compressing prompts is a way being studied with the hope of resulting in substantial savings in computing, memory, and storage and no substantial decrease in the general performance or quality of the output.

This paper, presented by researchers from Stanford University, proposes a novel technique for prompt compression called gisting, which trains an LM to compress prompts into smaller sets of “gist” tokens. With a view to reduce the price of the prompt, techniques like fine-tuning or distillation could be used to coach a model that will behave like the unique one without the prompt, but in that case, the model would must be re-trained for each recent prompt, which is way from ideal. The concept behind gisting, nonetheless, is to make use of a meta-learning approach to predict gist tokens from a prompt which might not require re-training the model for every task and would enable generalization to unseen instructions without additional training. This could include a discount in computational cost and would enable a prompt to be compressed, cached, and reused for compute efficiency. It could also allow users to suit more content into the limited context window.

The authors experimented with a straightforward way of achieving such a model – they used the LM itself (leveraging its pre-existing knowledge) to predict the gist tokens in the course of the instruction fine-tuning while modifying the Transformer attention masks. Given a (task, input) pair, they add gist tokens between the duty and the input and set the eye mask in the next way: the input tokens after the gist tokens cannot attend to any of the prompt tokens before the gist tokens (but they’ll attend to the gist tokens). Given the input and the output cannot attend to the prompt, this forces the model to compress the knowledge from the prompt into the gist tokens in between.
To coach the gist models, they needed a dataset with a big number of tasks, in order that they created a dataset that they called Alpaca+, which combined the info from two existing instruction tuning datasets (Standford Alpaca and Self-Instruct) which totaled greater than 130k examples. They then held out 3 validation splits to have the ability to validate the model after training which had Seen, Unseen, and hand-crafted Human prompts. This fashion, they were in a position to test the generalization to unseen instructions, with the Human split posing a good stronger generalization challenge. In addition they used multiple LM architectures (namely LLaMA-7Bm, a decoder-only GPT-style model, and FLAN-T5-XXL) and trained gist models with a various variety of gist tokens (1, 2, 5, or 10). Nonetheless, the outcomes showed that models were generally insensitive to the variety of gist tokens, in some cases even showing that a bigger variety of tokens was actually detrimental to performance. They, subsequently, used a single gist model for the remaining of the experiments.

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To evaluate the standard of the prompt compression, they calibrated performance against a positive control, which was effectively an ordinary instruction finetuning, which provided an upper sure on performance, and a negative control where the model wouldn’t have access to the instruction in any respect, leading to random gist tokens, which provided a lower sure on performance. To match the outputs of their models to the positive control and measure a win rate against it, they asked ChatGPT to decide on which response was higher, explaining its reasoning. In addition they used a straightforward lexical overlap statistic called ROUGE-L (a metric that measures similarities between generated text and human-written instructions in open-ended instruction fine-tuning). A 50% win rate indicates that the model is of comparable quality to a model that does no prompt compression.

The outcomes showed that on Seen instructions, the gist models performed very closely to the positive control models with 48.6% (LLaMA) and 50.8% (FLAN-T5) win rates. More importantly, they were in a position to show that the gist models had competitive generalizations to unseen prompts, with 49.7% (LLaMA) and 46.2% (FLAN-T5) win rates. Only on essentially the most difficult Human split they saw slight drops in win rates (but still competitive) with 45.8% (LLaMA) and 42.5% (FLAN-T5). The marginally worse performance of the FLAN-T5 and the actual failure cases brought more hypotheses to be tested in future papers.

The researchers also investigated the potential efficiency gains that could be achieved through gisting, which was the first motivation for the study. The outcomes were highly encouraging, with gist caching resulting in a 40% reduction in FLOPs and 4-7% lower wall clock time in comparison with unoptimized models. While these improvements were found to be smaller for decoder-only language models, the researchers also demonstrated that gist models enabled a 26x compression of unseen prompts, providing considerable additional space within the input context window.

Overall, these findings illustrate the numerous potential of gisting for enhancing each the effectiveness and efficiency of specialised language models. The authors also suggest several promising directions for follow-up work on gisting. For instance, they stipulate that the biggest compute and efficiency gains from gisting will come from compressing longer prompts and that “gist pretraining” could improve compression performance by first learning to compress arbitrary spans of natural language before learning prompt compression.


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Nathalie

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Nathalie Crevoisier holds a Bachelor’s and Master’s degree in Physics from Imperial College London. She spent a 12 months studying Applied Data Science, Machine Learning, and Web Analytics on the Ecole Polytechnique Federale de Lausanne (EPFL) as a part of her degree. During her studies, she developed a keen interest in AI, which led her to affix Meta (formerly Facebook) as a Data Scientist after graduating. During her four-year tenure at the corporate, Nathalie worked on various teams, including Ads, Integrity, and Workplace, applying cutting-edge data science and ML tools to resolve complex problems affecting billions of users. Looking for more independence and time to remain up-to-date with the newest AI discoveries, she recently decided to transition to a contract profession.


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