Improving Demonstration Diversity by Human-Free Fusing for Text-to-SQL

• URL: http://arxiv.org/abs/2402.10663v3
• Date: Wed, 26 Jun 2024 06:54:35 GMT
• Title: Improving Demonstration Diversity by Human-Free Fusing for Text-to-SQL
• Authors: Dingzirui Wang, Longxu Dou, Xuanliang Zhang, Qingfu Zhu, Wanxiang Che,
• Abstract summary: In this paper, we discuss how to measure and improve the diversity of the demonstrations for text-to-diversity research. We propose fusing iteratively for demonstrations (Fused) to build a high-diversity demonstration pool. Our method achieves an average improvement of 3.2% and 5.0% with and without human labeling on several mainstream datasets.
• Score: 51.48239006107272
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Currently, the in-context learning method based on large language models (LLMs) has become the mainstream of text-to-SQL research. Previous works have discussed how to select demonstrations related to the user question from a human-labeled demonstration pool. However, human labeling suffers from the limitations of insufficient diversity and high labeling overhead. Therefore, in this paper, we discuss how to measure and improve the diversity of the demonstrations for text-to-SQL. We present a metric to measure the diversity of the demonstrations and analyze the insufficient of the existing labeled data by experiments. Based on the above discovery, we propose fusing iteratively for demonstrations (Fused) to build a high-diversity demonstration pool through human-free multiple-iteration synthesis, improving diversity and lowering label cost. Our method achieves an average improvement of 3.2% and 5.0% with and without human labeling on several mainstream datasets, which proves the effectiveness of Fused.

Related papers

• Diversity-Oriented Data Augmentation with Large Language Models [9.548912625579947]
  We propose a textbfunderlineDi-textbfunderlineoriented data textbfunderlineAugmentation framework (textbfDoAug) Specifically, we utilize a diversity-oriented fine-tuning approach to train an LLM as a diverse paraphraser, which is capable of augmenting textual datasets by generating diversified paraphrases. The results show that our fine-tuned LLM augmenter improves diversity while preserving label consistency, thereby enhancing the robustness and performance of downstream tasks.
  arXiv  Detail & Related papers  (2025-02-17T11:00:40Z)
• DEUCE: Dual-diversity Enhancement and Uncertainty-awareness for Cold-start Active Learning [54.35107462768146]
  Cold-start active learning (CSAL) selects valuable instances from an unlabeled dataset for manual annotation. Existing CSAL methods overlook weak classes and hard representative examples, resulting in biased learning. This paper proposes a novel dual-diversity enhancing and uncertainty-aware framework for CSAL.
  arXiv  Detail & Related papers  (2025-02-01T04:00:03Z)
• Scaling Data Diversity for Fine-Tuning Language Models in Human Alignment [84.32768080422349]
  Alignment with human preference prevents large language models from generating misleading or toxic content. We propose a new formulation of prompt diversity, implying a linear correlation with the final performance of LLMs after fine-tuning.
  arXiv  Detail & Related papers  (2024-03-17T07:08:55Z)
• Effects of diversity incentives on sample diversity and downstream model performance in LLM-based text augmentation [6.273933281069326]
  We investigate three text diversity incentive methods well established in crowdsourcing: taboo words, hints by previous outlier solutions, and chaining on previous outlier solutions. We show that diversity is most increased by taboo words, but downstream model performance is highest with hints.
  arXiv  Detail & Related papers  (2024-01-12T15:46:43Z)
• In-Context Demonstration Selection with Cross Entropy Difference [95.21947716378641]
  Large language models (LLMs) can use in-context demonstrations to improve performance on zero-shot tasks. We present a cross-entropy difference (CED) method for selecting in-context demonstrations.
  arXiv  Detail & Related papers  (2023-05-24T05:04:00Z)
• Eliciting Compatible Demonstrations for Multi-Human Imitation Learning [16.11830547863391]
  Imitation learning from human-provided demonstrations is a strong approach for learning policies for robot manipulation. Natural human behavior has a great deal of heterogeneity, with several optimal ways to demonstrate a task. This mismatch presents a problem for interactive imitation learning, where sequences of users improve on a policy by iteratively collecting new, possibly conflicting demonstrations. We show that we can both identify incompatible demonstrations via post-hoc filtering, and apply our compatibility measure to actively elicit compatible demonstrations from new users.
  arXiv  Detail & Related papers  (2022-10-14T19:37:55Z)
• SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parsers [61.48159785138462]
  This paper aims to improve the performance of text-to-dependence by exploring the intrinsic uncertainties in the neural network based approaches (called SUN) Extensive experiments on five benchmark datasets demonstrate that our method significantly outperforms competitors and achieves new state-of-the-art results.
  arXiv  Detail & Related papers  (2022-09-14T06:27:51Z)
• MEmoBERT: Pre-training Model with Prompt-based Learning for Multimodal Emotion Recognition [118.73025093045652]
  We propose a pre-training model textbfMEmoBERT for multimodal emotion recognition. Unlike the conventional "pre-train, finetune" paradigm, we propose a prompt-based method that reformulates the downstream emotion classification task as a masked text prediction. Our proposed MEmoBERT significantly enhances emotion recognition performance.
  arXiv  Detail & Related papers  (2021-10-27T09:57:00Z)
• Informed Sampling for Diversity in Concept-to-Text NLG [8.883733362171034]
  We propose an Imitation Learning approach to explore the level of diversity that a language generation model can reliably produce. Specifically, we augment the decoding process with a meta-classifier trained to distinguish which words at any given timestep will lead to high-quality output.
  arXiv  Detail & Related papers  (2020-04-29T17:43:24Z)

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.