2024 ExtensiblePromptsforLanguageMod
- (Ge et al., 2024) ⇒ Tao Ge, Hu Jing, Li Dong, Shaoguang Mao, Yan Xia, Xun Wang, Si-Qing Chen, and Furu Wei. (2024). “Extensible Prompts for Language Models on Zero-shot Language Style Customization.” In: Advances in Neural Information Processing Systems, 36.
Subject Headings: Continuous Prompt Engineering, LLM Prompt Generation.
Notes
To create a summary focused on the paper "Extensible Prompts for Language Models on Zero-shot Language Style Customization," I will follow the structured format with wikilinks as requested:
- It introduces Extensible Prompt (X-Prompt), a novel method of instructing LLMs using not only NL but also an extensible vocabulary of imaginary words, aiming to enhance prompt descriptiveness beyond traditional NL capabilities.
- It proposes the concept of registering new imaginary words to enable LLMs to comprehend concepts that are challenging to describe with NL, thus facilitating more descriptive and adaptive prompting mechanisms.
- I introduces the use of imaginary words to be out-of-distribution (OOD) robust, allowing them to be reused across various prompts and distinguishing X-Prompt from soft prompts which are tailored for in-distribution data.
- It introduces Context-Augmented Learning (CAL) as a technique to learn imaginary words for general usability, ensuring that these words function effectively in OOD scenarios, including unseen prompts.
- Through experiments on zero-shot language style customization, It demonstrates the potential of X-Prompt to bridge the communication gap between humans and LLMs, showcasing promising results that indicate its efficacy in facilitating advanced LLM interactions.
- It presents both automated and human evaluations to validate the effectiveness of X-Prompt in achieving its objectives, highlighting significant improvements in OOD settings and style transfer capabilities when compared to existing prompt tuning methods.
- It addresses a critical and emerging challenge in the field of NLP—the customization of LLMs for specific tasks or styles in a zero-shot manner, offering a groundbreaking approach that could pave the way for personalized and more capable LLMs in various applications.
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Abstract
We propose eXtensible Prompt (X-Prompt) for prompting a large language model (LLM) beyond natural language (NL). X-Prompt instructs an LLM with not only NL but also an extensible vocabulary of imaginary words. Registering new imaginary words allows us to instruct the LLM to comprehend concepts that are difficult to describe with NL words, thereby making a prompt more descriptive. Also, these imaginary words are designed to be out-of-distribution (OOD) robust so that they can be (re) used like NL words in various prompts, distinguishing X-Prompt from soft prompt that is for fitting in-distribution data. We propose context-augmented learning (CAL) to learn imaginary words for general usability, enabling them to work properly in OOD (unseen) prompts. We experiment X-Prompt for zero-shot language style customization as a case study. The promising results of X-Prompt demonstrate its potential to facilitate advanced interaction beyond the natural language interface, bridging the communication gap between humans and LLMs.
4 Related Work
Since GPT (Brown et al., 2020) reveals that large pre-trained language models are good at zero-shot learning, much innovative research work has been proposed in recent years, ranging from prompt template design (i.e, engineering) (Schick and Schütze, 2020) to prompt mining (Jiang et al., 2019), generating (Gao et al., 2021; Ben-David et al., 2021) and scoring (Davison et al., 2019), finding that prompting the LLM with natural language can solve many downstream tasks as long as the prompt is well clear and rewritten for the model (Gonen et al., 2022).
As natural language prompts’ descriptive capability is limited, there is another branch of research studying continuous prompts (Li and Liang, 2021; Lester et al., 2021; Liu et al., 2021; Han et al., 2022; Hu et al., 2021) for fitting downstream tasks. However, these approaches are mainly for fitting ID task data with little consideration of OOD robustness, which means that their learned continuous prompts can hardly be used for OOD tasks or data.
Recently, Gal et al. (2022) proposed Textual Inversion in the multimodal context, which learns a virtual token to represent an object from an image and reveals that the learned virtual token can be used in unseen prompts for creative image generation (Kumari et al., 2022). X-Prompt is inspired by Gal et al. (2022), trying to learn OOD robust imaginary words to represent what natural language hardly describes to further expand zero-shot learning capabilities for the LLM, although we find it much more challenging to achieve this in NLP than text2image generation, which motivates us to propose context-augmented learning (CAL). To the best of our knowledge, our work is one of the earliest explorations in this direction in the NLP community.
5 Conclusion and Future Work
We propose X-Prompt, an extensible interface for prompting a large language model beyond natural language. X-Prompt can expand in-context learning capabilities to handle more complex instructions for language model customization and may open up many exciting opportunities, such as creative language generation, patching language models with new knowledge of entities (Zaporojets et al., 2022) and events (Ge et al., 2018), and detoxifying and debiasing in language generation (Welbl et al., 2021), far beyond style customization as demonstrated in this work, approaching advanced interaction between humans and large language models.
For future work, we plan to investigate how X-Prompt can facilitate more complex decoding and prompting methods (Wei et al., 2022b; Yao et al., 2022; Wang et al., 2023) to minimize the interaction effort between humans and large language models.
References
- Brown et al., 2020: This reference is related to the foundational work by Brown et al. on GPT, showcasing the capability of large pre-trained language models in zero-shot learning.
- Schick and Schütze, 2020: This reference discusses prompt template design or engineering, contributing to the field of prompt-based learning with natural language models.
- Jiang et al., 2019: Focuses on prompt mining, a method to improve the efficiency and effectiveness of language model prompting.
- Gao et al., 2021; Ben-David et al., 2021: These references explore prompt generation, expanding on how dynamically generated prompts can enhance model performance on various tasks.
- Davison et al., 2019: Examines prompt scoring, a technique to evaluate and optimize the prompts used for language models.
- Gonen et al., 2022: Discusses the importance of clear and well-formulated prompts in enabling language models to solve downstream tasks effectively.
- Li and Liang, 2021; Lester et al., 2021; Liu et al., 2021; Han et al., 2022; Hu et al., 2021: These references collectively cover studies on continuous prompts, focusing on their application for fitting downstream tasks and their limitations regarding OOD robustness.
- Gal et al., (2022): Introduces Textual Inversion in a multimodal context, a technique for learning virtual tokens to represent specific objects or concepts, inspiring the development of X-Prompt.
- Kumari et al., 2022: Builds on the concept of Textual Inversion by applying learned virtual tokens in unseen prompts for creative image generation.
- Zaporojets et al., 2022; Ge et al., 2018; Welbl et al., 2021: Discuss potential applications of X-Prompt in creative language generation, integrating new knowledge into language models, and addressing biases.
- Wei et al., 2022b; Yao et al., 2022; Wang et al., 2023: Mentioned as part of future work, focusing on how X-Prompt can support complex decoding and prompting methods to enhance human-model interaction.
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2024 ExtensiblePromptsforLanguageMod | Furu Wei Li Dong Tao Ge Hu Jing Shaoguang Mao Yan Xia Xun Wang Si-Qing Chen | Extensible Prompts for Language Models on Zero-shot Language Style Customization | 2024 |