Verbalized Machine Learning: Revisiting Machine Learning with Language Models
- URL: http://arxiv.org/abs/2406.04344v2
- Date: Sat, 19 Oct 2024 17:24:27 GMT
- Title: Verbalized Machine Learning: Revisiting Machine Learning with Language Models
- Authors: Tim Z. Xiao, Robert Bamler, Bernhard Schölkopf, Weiyang Liu,
- Abstract summary: We introduce the framework of verbalized machine learning (VML)
VML constrains the parameter space to be human-interpretable natural language.
We empirically verify the effectiveness of VML, and hope that VML can serve as a stepping stone to stronger interpretability.
- Score: 63.10391314749408
- License:
- Abstract: Motivated by the progress made by large language models (LLMs), we introduce the framework of verbalized machine learning (VML). In contrast to conventional machine learning (ML) models that are typically optimized over a continuous parameter space, VML constrains the parameter space to be human-interpretable natural language. Such a constraint leads to a new perspective of function approximation, where an LLM with a text prompt can be viewed as a function parameterized by the text prompt. Guided by this perspective, we revisit classical ML problems, such as regression and classification, and find that these problems can be solved by an LLM-parameterized learner and optimizer. The major advantages of VML include (1) easy encoding of inductive bias: prior knowledge about the problem and hypothesis class can be encoded in natural language and fed into the LLM-parameterized learner; (2) automatic model class selection: the optimizer can automatically select a model class based on data and verbalized prior knowledge, and it can update the model class during training; and (3) interpretable learner updates: the LLM-parameterized optimizer can provide explanations for why an update is performed. We empirically verify the effectiveness of VML, and hope that VML can serve as a stepping stone to stronger interpretability.
Related papers
- One Token Can Help! Learning Scalable and Pluggable Virtual Tokens for Retrieval-Augmented Large Language Models [67.49462724595445]
Retrieval-augmented generation (RAG) is a promising way to improve large language models (LLMs)
We propose a novel method that involves learning scalable and pluggable virtual tokens for RAG.
arXiv Detail & Related papers (2024-05-30T03:44:54Z) - Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning [35.03338699349037]
We propose a novel in-context learning framework, FeatLLM, which employs Large Language Models as feature engineers.
FeatLLM generates high-quality rules, significantly (10% on average) outperforming alternatives such as TabLLM and STUNT.
arXiv Detail & Related papers (2024-04-15T06:26:08Z) - Characterizing Truthfulness in Large Language Model Generations with
Local Intrinsic Dimension [63.330262740414646]
We study how to characterize and predict the truthfulness of texts generated from large language models (LLMs)
We suggest investigating internal activations and quantifying LLM's truthfulness using the local intrinsic dimension (LID) of model activations.
arXiv Detail & Related papers (2024-02-28T04:56:21Z) - Harnessing Large Language Models as Post-hoc Correctors [6.288056740658763]
We show that an LLM can work as a post-hoc corrector to propose corrections for the predictions of an arbitrary Machine Learning model.
We form a contextual knowledge database by incorporating the dataset's label information and the ML model's predictions on the validation dataset.
Our experimental results on text analysis and the challenging molecular predictions show that model improves the performance of a number of models by up to 39%.
arXiv Detail & Related papers (2024-02-20T22:50:41Z) - CLOMO: Counterfactual Logical Modification with Large Language Models [109.60793869938534]
We introduce a novel task, Counterfactual Logical Modification (CLOMO), and a high-quality human-annotated benchmark.
In this task, LLMs must adeptly alter a given argumentative text to uphold a predetermined logical relationship.
We propose an innovative evaluation metric, the Self-Evaluation Score (SES), to directly evaluate the natural language output of LLMs.
arXiv Detail & Related papers (2023-11-29T08:29:54Z) - LLM-augmented Preference Learning from Natural Language [19.700169351688768]
Large Language Models (LLMs) are equipped to deal with larger context lengths.
LLMs can consistently outperform the SotA when the target text is large.
Few-shot learning yields better performance than zero-shot learning.
arXiv Detail & Related papers (2023-10-12T17:17:27Z) - Explaining Emergent In-Context Learning as Kernel Regression [61.57151500616111]
Large language models (LLMs) have initiated a paradigm shift in transfer learning.
In this paper, we investigate the reason why a transformer-based language model can accomplish in-context learning after pre-training.
We find that during ICL, the attention and hidden features in LLMs match the behaviors of a kernel regression.
arXiv Detail & Related papers (2023-05-22T06:45:02Z) - Differentially Private Decoding in Large Language Models [14.221692239892207]
We propose a simple, easy to interpret, and computationally lightweight perturbation mechanism to be applied to an already trained model at the decoding stage.
Our perturbation mechanism is model-agnostic and can be used in conjunction with any Large Language Model.
arXiv Detail & Related papers (2022-05-26T20:50:58Z) - Transfer Learning without Knowing: Reprogramming Black-box Machine
Learning Models with Scarce Data and Limited Resources [78.72922528736011]
We propose a novel approach, black-box adversarial reprogramming (BAR), that repurposes a well-trained black-box machine learning model.
Using zeroth order optimization and multi-label mapping techniques, BAR can reprogram a black-box ML model solely based on its input-output responses.
BAR outperforms state-of-the-art methods and yields comparable performance to the vanilla adversarial reprogramming method.
arXiv Detail & Related papers (2020-07-17T01:52:34Z)
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.