MetaPrompting: Learning to Learn Better Prompts
Authors: Yutai Hou, Hongyuan Dong, Xinghao Wang, Bohan Li, Wanxiang Che
Abstract
Prompting method is regarded as one of the crucial progress for few-shot nature language processing. Recent research on prompting moves from discrete tokens based ``hard prompts'' to continuous ``soft prompts'', which employ learnable vectors as pseudo prompt tokens and achieve better performance. Though showing promising prospects, these soft-prompting methods are observed to rely heavily on good initialization to take effect. Unfortunately, obtaining a perfect initialization for soft prompts requires understanding of inner language models working and elaborate design, which is no easy task and has to restart from scratch for each new task. To remedy this, we propose a generalized soft prompting method called MetaPrompting, which adopts the well-recognized model-agnostic meta-learning algorithm to automatically find better prompt initialization that facilitates fast adaptation to new prompting tasks.Extensive experiments show MetaPrompting tackles soft prompt initialization problem and brings significant improvement on four different datasets (over 6 points improvement in accuracy for 1-shot setting), achieving new state-of-the-art performance.
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On Meta-PromptingAdrian de Wynter, Xun Wang, Qilong Gu et al. · 2023 · Computation and Language · AI summary available
Abstract
Modern generative language models are capable of interpreting input strings as instructions, or prompts, and carry out tasks based on them. Many approaches to prompting and pre-training these models involve the automated generation of these prompts: meta-prompting, or prompting to obtain prompts. We propose a theoretical framework based on category theory to generalize and describe them. This framework is flexible enough to account for stochasticity, and allows us to obtain formal results around task agnosticity and equivalence of various meta-prompting approaches. Experimentally, we test our framework in two active areas of model research: creativity and ideation. We find that user preferen…
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Prompt Engineering a Prompt EngineerQinyuan Ye, Maxamed Axmed, Reid Pryzant et al. · 2023 · Computation and Language · AI summary available
Abstract
Prompt engineering is a challenging yet crucial task for optimizing the performance of large language models (LLMs). It requires complex reasoning to examine the model's errors, hypothesize what is missing or misleading in the current prompt, and communicate the task with clarity. While recent works indicate that LLMs can be meta-prompted to perform automatic prompt engineering, their potentials may not be fully untapped due to the lack of sufficient guidance to elicit complex reasoning capabilities in LLMs in the meta-prompt. In this work, we investigate the problem of "prompt engineering a prompt engineer" -- constructing a meta-prompt that more effectively guides LLMs to perform automatic…
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Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language ProcessingPengfei Liu, Weizhe Yuan, Jinlan Fu et al. · 2021 · Computation and Language · AI summary available
Abstract
This paper surveys and organizes research works in a new paradigm in natural language processing, which we dub "prompt-based learning". Unlike traditional supervised learning, which trains a model to take in an input x and predict an output y as P(y|x), prompt-based learning is based on language models that model the probability of text directly. To use these models to perform prediction tasks, the original input x is modified using a template into a textual string prompt x' that has some unfilled slots, and then the language model is used to probabilistically fill the unfilled information to obtain a final string x, from which the final output y can be derived. This framework is powerful an…
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A Brief History of Prompt: Leveraging Language Models. (Through Advanced Prompting)Golam Md Muktadir · 2023 · Computation and Language · AI summary available
Abstract
This paper presents a comprehensive exploration of the evolution of prompt engineering and generation in the field of natural language processing (NLP). Starting from the early language models and information retrieval systems, we trace the key developments that have shaped prompt engineering over the years. The introduction of attention mechanisms in 2015 revolutionized language understanding, leading to advancements in controllability and context-awareness. Subsequent breakthroughs in reinforcement learning techniques further enhanced prompt engineering, addressing issues like exposure bias and biases in generated text. We examine the significant contributions in 2018 and 2019, focusing on…
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Prompting Large Language Model for Machine Translation: A Case StudyBiao Zhang, Barry Haddow, Alexandra Birch · 2023 · Computation and Language · AI summary available
Abstract
Research on prompting has shown excellent performance with little or even no supervised training across many tasks. However, prompting for machine translation is still under-explored in the literature. We fill this gap by offering a systematic study on prompting strategies for translation, examining various factors for prompt template and demonstration example selection. We further explore the use of monolingual data and the feasibility of cross-lingual, cross-domain, and sentence-to-document transfer learning in prompting. Extensive experiments with GLM-130B (Zeng et al., 2022) as the testbed show that 1) the number and the quality of prompt examples matter, where using suboptimal examples …
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Structured Prompt TuningChi-Liang Liu, Hung-yi Lee, Wen-tau Yih · 2022 · Computation and Language · AI summary available
Abstract
We propose structured prompt tuning, a simple and effective method to improve prompt tuning. Instead of prepending a sequence of tunable embeddings to the input, we generate the soft prompt embeddings through a hypernetwork. Our approach subsumes the standard prompt tuning, allows more flexibility in model design and can be applied to both single-task and multi-task training settings. Empirically, structured prompt tuning shows a gain of +1.2$~1.5 points on the GLUE benchmark and is less sensitive to the change of learning rate, compared to standard prompt tuning.
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Automatic Prompt Optimization with "Gradient Descent" and Beam SearchReid Pryzant, Dan Iter, Jerry Li et al. · 2023 · Computation and Language · Not summarized yet
Abstract
Large Language Models (LLMs) have shown impressive performance as general purpose agents, but their abilities remain highly dependent on prompts which are hand written with onerous trial-and-error effort. We propose a simple and nonparametric solution to this problem, Automatic Prompt Optimization (APO), which is inspired by numerical gradient descent to automatically improve prompts, assuming access to training data and an LLM API. The algorithm uses minibatches of data to form natural language "gradients" that criticize the current prompt. The gradients are then "propagated" into the prompt by editing the prompt in the opposite semantic direction of the gradient. These gradient descent ste…
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Are Large Language Models Good Prompt Optimizers?Ruotian Ma, Xiaolei Wang, Xin Zhou et al. · 2024 · Computation and Language · AI summary available
Abstract
LLM-based Automatic Prompt Optimization, which typically utilizes LLMs as Prompt Optimizers to self-reflect and refine prompts, has shown promising performance in recent studies. Despite the success, the underlying mechanism of this approach remains unexplored, and the true effectiveness of LLMs as Prompt Optimizers requires further validation. In this work, we conducted a comprehensive study to uncover the actual mechanism of LLM-based Prompt Optimization. Our findings reveal that the LLM optimizers struggle to identify the true causes of errors during reflection, tending to be biased by their own prior knowledge rather than genuinely reflecting on the errors. Furthermore, even when the ref…
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