Large Language Models as Annotators: Enhancing Generalization of NLP Models at Minimal Cost

• URL: http://arxiv.org/abs/2306.15766v1
• Date: Tue, 27 Jun 2023 19:29:55 GMT
• Title: Large Language Models as Annotators: Enhancing Generalization of NLP Models at Minimal Cost
• Authors: Parikshit Bansal, Amit Sharma
• Abstract summary: We study the use of large language models (LLMs) for annotating inputs and improving the generalization of NLP models. We propose a sampling strategy based on the difference in prediction scores between the base model and the finetuned NLP model.
• Score: 6.662800021628275
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: State-of-the-art supervised NLP models achieve high accuracy but are also susceptible to failures on inputs from low-data regimes, such as domains that are not represented in training data. As an approximation to collecting ground-truth labels for the specific domain, we study the use of large language models (LLMs) for annotating inputs and improving the generalization of NLP models. Specifically, given a budget for LLM annotations, we present an algorithm for sampling the most informative inputs to annotate and retrain the NLP model. We find that popular active learning strategies such as uncertainty-based sampling do not work well. Instead, we propose a sampling strategy based on the difference in prediction scores between the base model and the finetuned NLP model, utilizing the fact that most NLP models are finetuned from a base model. Experiments with classification (semantic similarity) and ranking (semantic search) tasks show that our sampling strategy leads to significant gains in accuracy for both the training and target domains.

Related papers

• Uncertainty Aware Learning for Language Model Alignment [97.36361196793929]
  We propose uncertainty-aware learning (UAL) to improve the model alignment of different task scenarios. We implement UAL in a simple fashion -- adaptively setting the label smoothing value of training according to the uncertainty of individual samples. Experiments on widely used benchmarks demonstrate that our UAL significantly and consistently outperforms standard supervised fine-tuning.
  arXiv  Detail & Related papers  (2024-06-07T11:37:45Z)
• Preference Learning Algorithms Do Not Learn Preference Rankings [62.335733662381884]
  We study the conventional wisdom that preference learning trains models to assign higher likelihoods to more preferred outputs than less preferred outputs. We find that most state-of-the-art preference-tuned models achieve a ranking accuracy of less than 60% on common preference datasets.
  arXiv  Detail & Related papers  (2024-05-29T21:29:44Z)
• Querying Easily Flip-flopped Samples for Deep Active Learning [63.62397322172216]
  Active learning is a machine learning paradigm that aims to improve the performance of a model by strategically selecting and querying unlabeled data. One effective selection strategy is to base it on the model's predictive uncertainty, which can be interpreted as a measure of how informative a sample is. This paper proposes the it least disagree metric (LDM) as the smallest probability of disagreement of the predicted label.
  arXiv  Detail & Related papers  (2024-01-18T08:12:23Z)
• Universal Domain Adaptation from Foundation Models: A Baseline Study [58.51162198585434]
  We make empirical studies of state-of-the-art UniDA methods using foundation models. We introduce textitCLIP distillation, a parameter-free method specifically designed to distill target knowledge from CLIP models. Although simple, our method outperforms previous approaches in most benchmark tasks.
  arXiv  Detail & Related papers  (2023-05-18T16:28:29Z)
• Learning New Tasks from a Few Examples with Soft-Label Prototypes [18.363177410917597]
  We propose a novel few-shot learning approach based on soft-label prototypes (SLPs) We focus on learning previously unseen NLP tasks from very few examples (4, 8, 16) per class. We experimentally demonstrate that our approach achieves superior performance on the majority of tested tasks in this data-lean setting.
  arXiv  Detail & Related papers  (2022-10-31T16:06:48Z)
• Predicting Fine-Tuning Performance with Probing [18.129450295108423]
  This paper explores the utility of probing deep NLP models to extract a proxy signal widely used in model development. We find that it is possible to use the accuracies of only three probing tests to predict the fine-tuning performance with errors $40%$ - $80%$ smaller than baselines.
  arXiv  Detail & Related papers  (2022-10-13T20:58:14Z)
• Improving Pre-trained Language Model Fine-tuning with Noise Stability Regularization [94.4409074435894]
  We propose a novel and effective fine-tuning framework, named Layerwise Noise Stability Regularization (LNSR) Specifically, we propose to inject the standard Gaussian noise and regularize hidden representations of the fine-tuned model. We demonstrate the advantages of the proposed method over other state-of-the-art algorithms including L2-SP, Mixout and SMART.
  arXiv  Detail & Related papers  (2022-06-12T04:42:49Z)
• Evaluating the Robustness of Neural Language Models to Input Perturbations [7.064032374579076]
  In this study, we design and implement various types of character-level and word-level perturbation methods to simulate noisy input texts. We investigate the ability of high-performance language models such as BERT, XLNet, RoBERTa, and ELMo in handling different types of input perturbations. The results suggest that language models are sensitive to input perturbations and their performance can decrease even when small changes are introduced.
  arXiv  Detail & Related papers  (2021-08-27T12:31:17Z)
• Model Explainability in Deep Learning Based Natural Language Processing [0.0]
  We reviewed and compared some popular machine learning model explainability methodologies. We applied one of the NLP explainability methods to a NLP classification model. We identified some common issues due to the special natures of NLP models.
  arXiv  Detail & Related papers  (2021-06-14T13:23:20Z)
• Bayesian Active Learning with Pretrained Language Models [9.161353418331245]
  Active Learning (AL) is a method to iteratively select data for annotation from a pool of unlabeled data. Previous AL approaches have been limited to task-specific models that are trained from scratch at each iteration. We introduce BALM; Bayesian Active Learning with pretrained language models.
  arXiv  Detail & Related papers  (2021-04-16T19:07:31Z)
• Explaining and Improving Model Behavior with k Nearest Neighbor Representations [107.24850861390196]
  We propose using k nearest neighbor representations to identify training examples responsible for a model's predictions. We show that kNN representations are effective at uncovering learned spurious associations. Our results indicate that the kNN approach makes the finetuned model more robust to adversarial inputs.
  arXiv  Detail & Related papers  (2020-10-18T16:55:25Z)

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.