The Text-to-SQL capabilities of large language allow users to interact with databases using natural language. While current models struggle with handling complex queries, especially involving multi-table joins and reasoning. To address this gap, we propose to construct a model, namely SAC-SQL, with synthetic training samples followed by a structure-aware curriculum learning framework for enhancing SQL generation. Our approach begins with a supervised fine-tuning (SFT) stage, where we train open-source models on a synthetically constructed, cross-domain SQL dataset with diverse structural patterns. Moreover, we introduce a unified structure difficulty scoring function to partition the training samples into non-overlapping curriculum phases, guiding the model progressively learning from simpler to more complex SQL structures. Extensive experiments are conducted and the results show that SAC-SQL achieves better results than the baselines, and significantly narrows the performance gap between open-source and close-source models on Spider and Bird benchmarks.

Full-parameter fine-tuning (FPFT) has become the go-to choice for adapting language models (LMs) to downstream tasks due to its excellent performance. As LMs grow in size, fine-tuning the full parameters of LMs requires a prohibitively large amount of GPU memory. Existing approaches utilize zeroth-order optimizer to conserve GPU memory, which potentially compromises the performance of LMs as non-zero order optimizers tend to converge more readily on most downstream tasks. We propose a novel, memory-efficient, optimizer-independent, end-to-end hierarchical fine-tuning strategy, HiFT, which only updates a subset of parameters at each training step. HiFT significantly reduces the amount of gradients and optimizer state parameters residing in GPU memory at the same time, thereby reducing GPU memory usage. Our results demonstrate that: (1) HiFT achieves comparable performance with parameter-efficient fine-tuning and standard FPFT. (2) Results on six models show that HiFT reduces the number of trainable parameters by about 89.18% on average compared to FPFT. (3) HiFT supports FPFT of 7B models for 24G GPU memory devices under mixed precision without using any memory saving techniques. (4) HiFT supports various optimizers including AdamW, AdaGrad, SGD, etc. The source code link is https://github.com/misonsky/HiFT.

Prompt tuning (PT) which only tunes the embeddings of an additional sequence of tokens per task, keeping the pre-trained language model (PLM) frozen, has shown remarkable performance in few-shot learning. Despite this, PT has been shown to rely heavily on good initialization of the prompt embeddings. In this work, we study meta prompt tuning (MPT) to systematically explore how meta-learning can help improve (if it can) cross-task generalization in PT through learning to initialize the prompt embeddings from other relevant tasks. We empirically analyze a representative set of meta learning algorithms in a wide range of adaptation settings with different source/target task configurations on a large set of few-shot tasks. With extensive experiments and analysis, we demonstrate the effectiveness of MPT. We find the improvement to be significant particularly on classification tasks. For other kinds of tasks such as question answering, we observe that while MPT can outperform PT in most cases, it does not always outperform multi-task learning. We further provide an in-depth analysis from the perspective of task similarity.

With the evolution of Knowledge Graphs (KGs), new entities emerge which are not seen before. Representation learning of KGs in such an inductive setting aims to capture and transfer the structural patterns from existing entities to new entities. However, the performance of existing methods in inductive KGs are limited by sparsity and implicit transfer. In this paper, we propose VMCL, a Contrastive Learning (CL) framework with graph guided Variational autoencoder on Meta-KGs in the inductive setting. We first propose representation generation to capture the encoded and generated representations of entities, where the generated variations can densify representations with complementary features. Then, we design two CL objectives that work across entities and meta-KGs to simulate the transfer mode. With extensive experiments we demonstrate that our proposed VMCL can significantly outperform previous state-of-the-art baselines.