Eliciting Textual Descriptions from Representations of Continuous Prompts

• URL: http://arxiv.org/abs/2410.11660v1
• Date: Tue, 15 Oct 2024 14:46:11 GMT
• Title: Eliciting Textual Descriptions from Representations of Continuous Prompts
• Authors: Dana Ramati, Daniela Gottesman, Mor Geva,
• Abstract summary: We propose a new approach to interpret continuous prompts that elicits textual descriptions from their representations during model inference. We show our method often yields accurate task descriptions which become more faithful as task performance increases. InSPEcT can be leveraged to debug unwanted properties in continuous prompts and inform developers on ways to mitigate them.
• Score: 11.489611613744724
• License:
• Abstract: Continuous prompts, or "soft prompts", are a widely-adopted parameter-efficient tuning strategy for large language models, but are often less favorable due to their opaque nature. Prior attempts to interpret continuous prompts relied on projecting individual prompt tokens onto the vocabulary space. However, this approach is problematic as performant prompts can yield arbitrary or contradictory text, and it interprets prompt tokens individually. In this work, we propose a new approach to interpret continuous prompts that elicits textual descriptions from their representations during model inference. Using a Patchscopes variant (Ghandeharioun et al., 2024) called InSPEcT over various tasks, we show our method often yields accurate task descriptions which become more faithful as task performance increases. Moreover, an elaborated version of InSPEcT reveals biased features in continuous prompts, whose presence correlates with biased model predictions. Providing an effective interpretability solution, InSPEcT can be leveraged to debug unwanted properties in continuous prompts and inform developers on ways to mitigate them.

Related papers

• Hard Prompts Made Interpretable: Sparse Entropy Regularization for Prompt Tuning with RL [29.01858866450715]
  We present RLPrompt, which aims to find optimal prompt tokens leveraging soft Q-learning. While the results show promise, we have observed that the prompts frequently appear unnatural, which impedes their interpretability. We address this limitation by using sparse Tsallis entropy regularization, a principled approach to filtering out unlikely tokens from consideration.
  arXiv  Detail & Related papers  (2024-07-20T03:10:19Z)
• Continuous Prompt Generation from Linear Combination of Discrete Prompt Embeddings [0.0]
  We present a novel method of constructing continuous prompts via discrete prompt embeddings and evaluate improvements to continuous prompt interpretability and inference accuracy. For a set of manually designed discrete prompts $mathcalD$, which we tokenize and embed each into tensor form, we train a model to predict the weights such that the linear combinations of those prompts correspond to higher performance on natural language understanding tasks.
  arXiv  Detail & Related papers  (2023-12-16T05:02:06Z)
• Self-regulating Prompts: Foundational Model Adaptation without Forgetting [112.66832145320434]
  We introduce a self-regularization framework for prompting called PromptSRC. PromptSRC guides the prompts to optimize for both task-specific and task-agnostic general representations.
  arXiv  Detail & Related papers  (2023-07-13T17:59:35Z)
• On the Role of Attention in Prompt-tuning [90.97555030446563]
  We study prompt-tuning for one-layer attention architectures and study contextual mixture-models. We show that softmax-prompt-attention is provably more expressive than softmax-self-attention and linear-prompt-attention. We also provide experiments that verify our theoretical insights on real datasets and demonstrate how prompt-tuning enables the model to attend to context-relevant information.
  arXiv  Detail & Related papers  (2023-06-06T06:23:38Z)
• Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models [103.61066310897928]
  Recent text-to-image generative models have demonstrated an unparalleled ability to generate diverse and creative imagery guided by a target text prompt. While revolutionary, current state-of-the-art diffusion models may still fail in generating images that fully convey the semantics in the given text prompt. We analyze the publicly available Stable Diffusion model and assess the existence of catastrophic neglect, where the model fails to generate one or more of the subjects from the input prompt. We introduce the concept of Generative Semantic Nursing (GSN), where we seek to intervene in the generative process on the fly during inference time to improve the faithfulness
  arXiv  Detail & Related papers  (2023-01-31T18:10:38Z)
• Generative Prompt Tuning for Relation Classification [21.027631157115135]
  We propose a novel generative prompt tuning method to reformulate relation classification as an infilling problem. In addition, we design entity-guided decoding and discriminative relation scoring to generate and align relations effectively and efficiently during inference.
  arXiv  Detail & Related papers  (2022-10-22T12:40:23Z)
• Bayesian Prompt Learning for Image-Language Model Generalization [64.50204877434878]
  We use the regularization ability of Bayesian methods to frame prompt learning as a variational inference problem. Our approach regularizes the prompt space, reduces overfitting to the seen prompts and improves the prompt generalization on unseen prompts. We demonstrate empirically on 15 benchmarks that Bayesian prompt learning provides an appropriate coverage of the prompt space.
  arXiv  Detail & Related papers  (2022-10-05T17:05:56Z)
• RLPrompt: Optimizing Discrete Text Prompts With Reinforcement Learning [84.75064077323098]
  This paper proposes RLPrompt, an efficient discrete prompt optimization approach with reinforcement learning (RL) RLPrompt is flexibly applicable to different types of LMs, such as masked gibberish (e.g., grammaBERT) and left-to-right models (e.g., GPTs) Experiments on few-shot classification and unsupervised text style transfer show superior performance over a wide range of existing finetuning or prompting methods.
  arXiv  Detail & Related papers  (2022-05-25T07:50:31Z)
• PROMPT WAYWARDNESS: The Curious Case of Discretized Interpretation of Continuous Prompts [99.03864962014431]
  Fine-tuning continuous prompts for target tasks has emerged as a compact alternative to full model fine-tuning. In practice, we observe a "wayward" behavior between the task solved by continuous prompts and their nearest neighbor.
  arXiv  Detail & Related papers  (2021-12-15T18:55:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.