Leveraging Multiple Teachers for Test-Time Adaptation of Language-Guided Classifiers

• URL: http://arxiv.org/abs/2311.07538v1
• Date: Mon, 13 Nov 2023 18:28:25 GMT
• Title: Leveraging Multiple Teachers for Test-Time Adaptation of Language-Guided Classifiers
• Authors: Kangda Wei, Sayan Ghosh, Rakesh R. Menon, Shashank Srivastava
• Abstract summary: We introduce TALC, a framework that adapts a language-guided classifier for a new task during inference when provided with explanations from multiple teachers and unlabeled test examples. Our results show that TALC consistently outperforms a competitive baseline from prior work by an impressive 9.3% (relative improvement)
• Score: 21.55294900536358
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent approaches have explored language-guided classifiers capable of classifying examples from novel tasks when provided with task-specific natural language explanations, instructions or prompts (Sanh et al., 2022; R. Menon et al., 2022). While these classifiers can generalize in zero-shot settings, their task performance often varies substantially between different language explanations in unpredictable ways (Lu et al., 2022; Gonen et al., 2022). Also, current approaches fail to leverage unlabeled examples that may be available in many scenarios. Here, we introduce TALC, a framework that uses data programming to adapt a language-guided classifier for a new task during inference when provided with explanations from multiple teachers and unlabeled test examples. Our results show that TALC consistently outperforms a competitive baseline from prior work by an impressive 9.3% (relative improvement). Further, we demonstrate the robustness of TALC to variations in the quality and quantity of provided explanations, highlighting its potential in scenarios where learning from multiple teachers or a crowd is involved. Our code is available at: https://github.com/WeiKangda/TALC.git.

Related papers

• PromptRefine: Enhancing Few-Shot Performance on Low-Resource Indic Languages with Example Selection from Related Example Banks [57.86928556668849]
  Large Language Models (LLMs) have recently demonstrated impressive few-shot learning capabilities through in-context learning (ICL) ICL performance is highly dependent on the choice of few-shot demonstrations, making the selection of the most optimal examples a persistent research challenge. In this work, we propose PromptRefine, a novel Alternating Minimization approach for example selection that improves ICL performance on low-resource Indic languages.
  arXiv  Detail & Related papers  (2024-12-07T17:51:31Z)
• Adapting Vision-Language Models to Open Classes via Test-Time Prompt Tuning [50.26965628047682]
  Adapting pre-trained models to open classes is a challenging problem in machine learning. In this paper, we consider combining the advantages of both and come up with a test-time prompt tuning approach. Our proposed method outperforms all comparison methods on average considering both base and new classes.
  arXiv  Detail & Related papers  (2024-08-29T12:34:01Z)
• Language Models for Text Classification: Is In-Context Learning Enough? [54.869097980761595]
  Recent foundational language models have shown state-of-the-art performance in many NLP tasks in zero- and few-shot settings. An advantage of these models over more standard approaches is the ability to understand instructions written in natural language (prompts) This makes them suitable for addressing text classification problems for domains with limited amounts of annotated instances.
  arXiv  Detail & Related papers  (2024-03-26T12:47:39Z)
• EXnet: Efficient In-context Learning for Data-less Text classification [0.0]
  We present EXnet, a model specifically designed to perform in-context learning without limitations on the number of examples. We argue that in-context learning is an effective method to increase task accuracy, and providing examples facilitates cross-task generalization. With extensive experiments, we show that even our smallest model (15M parameters) generalizes to several unseen classification tasks and domains.
  arXiv  Detail & Related papers  (2023-05-24T01:40:57Z)
• Are Large Language Models Robust Coreference Resolvers? [17.60248310475889]
  We show that prompting for coreference can outperform current unsupervised coreference systems. Further investigations reveal that instruction-tuned LMs generalize surprisingly well across domains, languages, and time periods.
  arXiv  Detail & Related papers  (2023-05-23T19:38:28Z)
• In-Context Probing: Toward Building Robust Classifiers via Probing Large Language Models [5.5089506884366735]
  In this paper, we propose an alternative approach, which we term In-Context Probing (ICP) Similar to in-context learning, we contextualize the representation of the input with an instruction, but instead of decoding the output prediction, we probe the contextualized representation to predict the label. We show that ICP performs competitive or superior to finetuning and can be particularly helpful to build classifiers on top of smaller models.
  arXiv  Detail & Related papers  (2023-05-23T15:43:04Z)
• Efficiently Aligned Cross-Lingual Transfer Learning for Conversational Tasks using Prompt-Tuning [98.60739735409243]
  Cross-lingual transfer of language models trained on high-resource languages like English has been widely studied for many NLP tasks. We introduce XSGD for cross-lingual alignment pretraining, a parallel and large-scale multilingual conversation dataset. To facilitate aligned cross-lingual representations, we develop an efficient prompt-tuning-based method for learning alignment prompts.
  arXiv  Detail & Related papers  (2023-04-03T18:46:01Z)
• Improving Few-Shot Performance of Language Models via Nearest Neighbor Calibration [12.334422701057674]
  We propose a novel nearest-neighbor calibration framework for in-context learning. It is inspired by a phenomenon that the in-context learning paradigm produces incorrect labels when inferring training instances. Experiments on various few-shot text classification tasks demonstrate that our method significantly improves in-context learning.
  arXiv  Detail & Related papers  (2022-12-05T12:49:41Z)
• LASP: Text-to-Text Optimization for Language-Aware Soft Prompting of Vision & Language Models [67.19124099815645]
  We propose a novel Language-Aware Soft Prompting (LASP) learning method to alleviate base class overfitting. LASP is inherently amenable to including, during training, virtual classes, i.e. class names for which no visual samples are available. LASP matches and surpasses, for the first time, the accuracy on novel classes obtained by hand-crafted prompts and CLIP for 8 out of 11 test datasets.
  arXiv  Detail & Related papers  (2022-10-03T17:56:35Z)
• Selective Annotation Makes Language Models Better Few-Shot Learners [97.07544941620367]
  Large language models can perform in-context learning, where they learn a new task from a few task demonstrations. This work examines the implications of in-context learning for the creation of datasets for new natural language tasks. We propose an unsupervised, graph-based selective annotation method, voke-k, to select diverse, representative examples to annotate.
  arXiv  Detail & Related papers  (2022-09-05T14:01:15Z)
• CLUES: A Benchmark for Learning Classifiers using Natural Language Explanations [12.278877764015725]
  Supervised learning has traditionally focused on inductive learning by observing labeled examples of a task. In contrast, humans have ability to learn new concepts from language. We introduce CLUES, benchmark for learning using natural language ExplanationS. CLUES consists of 36 real-world and 144 synthetic classification tasks.
  arXiv  Detail & Related papers  (2022-04-14T17:54:46Z)

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.