Automated Few-shot Classification with Instruction-Finetuned Language Models

• URL: http://arxiv.org/abs/2305.12576v2
• Date: Sat, 21 Oct 2023 06:46:51 GMT
• Title: Automated Few-shot Classification with Instruction-Finetuned Language Models
• Authors: Rami Aly, Xingjian Shi, Kaixiang Lin, Aston Zhang, Andrew Gordon Wilson
• Abstract summary: We show that AuT-Few outperforms state-of-the-art few-shot learning methods. We also show that AuT-Few is the best ranking method across datasets on the RAFT few-shot benchmark.
• Score: 76.69064714392165
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: A particularly successful class of approaches for few-shot learning combines language models with prompts -- hand-crafted task descriptions that complement data samples. However, designing prompts by hand for each task commonly requires domain knowledge and substantial guesswork. We observe, in the context of classification tasks, that instruction finetuned language models exhibit remarkable prompt robustness, and we subsequently propose a simple method to eliminate the need for handcrafted prompts, named AuT-Few. This approach consists of (i) a prompt retrieval module that selects suitable task instructions from the instruction-tuning knowledge base, and (ii) the generation of two distinct, semantically meaningful, class descriptions and a selection mechanism via cross-validation. Over $12$ datasets, spanning $8$ classification tasks, we show that AuT-Few outperforms current state-of-the-art few-shot learning methods. Moreover, AuT-Few is the best ranking method across datasets on the RAFT few-shot benchmark. Notably, these results are achieved without task-specific handcrafted prompts on unseen tasks.

Related papers

• TransPrompt v2: A Transferable Prompting Framework for Cross-task Text Classification [37.824031151922604]
  We propose TransPrompt v2, a novel transferable prompting framework for few-shot learning across similar or distant text classification tasks. For learning across similar tasks, we employ a multi-task meta-knowledge acquisition (MMA) procedure to train a meta-learner. For learning across distant tasks, we inject the task type descriptions into the prompt, and capture the intra-type and inter-type prompt embeddings.
  arXiv  Detail & Related papers  (2023-08-29T04:16:57Z)
• MetricPrompt: Prompting Model as a Relevance Metric for Few-shot Text Classification [65.51149771074944]
  MetricPrompt eases verbalizer design difficulty by reformulating few-shot text classification task into text pair relevance estimation task. We conduct experiments on three widely used text classification datasets across four few-shot settings. Results show that MetricPrompt outperforms manual verbalizer and other automatic verbalizer design methods across all few-shot settings.
  arXiv  Detail & Related papers  (2023-06-15T06:51:35Z)
• AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotators [98.11286353828525]
  GPT-3.5 series models have demonstrated remarkable few-shot and zero-shot ability across various NLP tasks. We propose AnnoLLM, which adopts a two-step approach, explain-then-annotate. We build the first conversation-based information retrieval dataset employing AnnoLLM.
  arXiv  Detail & Related papers  (2023-03-29T17:03:21Z)
• STPrompt: Semantic-guided and Task-driven prompts for Effective Few-shot Classification [5.6205035780719275]
  We propose the STPrompt -Semantic-guided and Task-driven Prompt model. The proposed model achieves the state-of-the-art performance in five different datasets of few-shot text classification tasks.
  arXiv  Detail & Related papers  (2022-10-29T04:42:30Z)
• Instance-wise Prompt Tuning for Pretrained Language Models [72.74916121511662]
  Instance-wise Prompt Tuning (IPT) is the first prompt learning paradigm that injects knowledge from the input data instances to the prompts. IPT significantly outperforms task-based prompt learning methods, and achieves comparable performance to conventional finetuning with only 0.5% - 1.5% of tuned parameters.
  arXiv  Detail & Related papers  (2022-06-04T10:08:50Z)
• Improving Multi-task Generalization Ability for Neural Text Matching via Prompt Learning [54.66399120084227]
  Recent state-of-the-art neural text matching models (PLMs) are hard to generalize to different tasks. We adopt a specialization-generalization training strategy and refer to it as Match-Prompt. In specialization stage, descriptions of different matching tasks are mapped to only a few prompt tokens. In generalization stage, text matching model explores the essential matching signals by being trained on diverse multiple matching tasks.
  arXiv  Detail & Related papers  (2022-04-06T11:01:08Z)
• InstructionNER: A Multi-Task Instruction-Based Generative Framework for Few-shot NER [31.32381919473188]
  We propose a multi-task instruction-based generative framework, named InstructionNER, for low-resource named entity recognition. Specifically, we reformulate the NER task as a generation problem, which enriches source sentences with task-specific instructions and answer options, then inferences the entities and types in natural language. Experimental results show that our method consistently outperforms other baselines on five datasets in few-shot settings.
  arXiv  Detail & Related papers  (2022-03-08T07:56:36Z)
• CINS: Comprehensive Instruction for Few-shot Learning in Task-oriented Dialog Systems [56.302581679816775]
  This paper proposes Comprehensive Instruction (CINS) that exploits PLMs with task-specific instructions. We design a schema (definition, constraint, prompt) of instructions and their customized realizations for three important downstream tasks in ToD. Experiments are conducted on these ToD tasks in realistic few-shot learning scenarios with small validation data.
  arXiv  Detail & Related papers  (2021-09-10T03:23:06Z)

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.