Sentence representation learning is a fundamental task in NLP. Existing methods use contrastive learning (CL) to learn effective sentence representations, which benefit from high-quality contrastive data but require extensive human annotation. Large language models (LLMs) like ChatGPT and GPT4 can automatically generate such data. However, this alternative strategy also encounters challenges: 1) obtaining high-quality generated data from small-parameter LLMs is difficult, and 2) inefficient utilization of the generated data. To address these challenges, we propose a novel adaptive reinforcement tuning (ART) framework. Specifically, to address the first challenge, we introduce a reinforcement learning approach for fine-tuning small-parameter LLMs, enabling the generation of high-quality hard contrastive data without human feedback. To address the second challenge, we propose an adaptive iterative framework to guide the small-parameter LLMs to generate progressively harder samples through multiple iterations, thereby maximizing the utility of generated data. Experiments conducted on seven semantic text similarity tasks demonstrate that the sentence representation models trained using the synthetic data generated by our proposed method achieve state-of-the-art performance. Our code is available at https://github.com/WuNein/AdaptCL.

Recently, multimodal information extraction from social media posts has gained increasing attention in the natural language processing community. Despite their success, current approaches overestimate the significance of images. In this paper, we argue that different social media posts should consider different modalities for multimodal information extraction. Multimodal models cannot always outperform unimodal models. Some posts are more suitable for the multimodal model, while others are more suitable for the unimodal model. Therefore, we propose a general data splitting strategy to divide the social media posts into two sets so that these two sets can achieve better performance under the information extraction models of the corresponding modalities. Specifically, for an information extraction task, we first propose a data discriminator that divides social media posts into a multimodal and a unimodal set. Then we feed these sets into the corresponding models. Finally, we combine the results of these two models to obtain the final extraction results. Due to the lack of explicit knowledge, we use reinforcement learning to train the data discriminator. Experiments on two different multimodal information extraction tasks demonstrate the effectiveness of our method. The source code of this paper can be found in https://github.com/xubodhu/RDS.