With the continuously expanding parameters, efficiently adapting large language models to downstream tasks is crucial in resource-limited conditions. Many parameter-efficient fine-tuning methods have emerged to address this challenge. However, they lack flexibility, like LoRA requires manually selecting trainable parameters and rank size, (IA)³ can only scale the activations along columns, yielding inferior results due to less precise fine-tuning. To address these issues, we propose a novel method named AdaDHP with fewer parameters and finer granularity, which can adaptively select important parameters for each task. Specifically, we introduce two trainable vectors for each parameter and fine-tune the parameters through Hadamard product along both rows and columns. This significantly reduces the number of trainable parameters, with our parameter count capped at the lower limit of LoRA. Moreover, we design an adaptive parameter selection strategy to select important parameters for downstream tasks dynamically. This allows our method to flexibly remove unimportant parameters for downstream tasks. Finally, we demonstrate the superiority of our method on the T5-base model across 17 NLU tasks and on complex mathematical tasks with the Llama series models.

Generalized zero-shot intent detection (GZID) aims to recognize the labels of utterances from both seen and unseen intents by utilizing the knowledge learned from seen intents. Enhancing the generalization ability from seen intents to unseen intents is a key challenge in the GZID setting. Existing methods attempt to tackle this challenge by distinguishing unseen intents from seen intents or focusing on enhancing the model discriminability. However, the challenge is not solved substantially as they ignore to promote the representation learning ability of the model itself and neglect to strengthen the model adaptability to new tasks, resulting in overfitting on the seen intents. In this paper, we propose a pairwise prompt-based tuning model with parameter efficient fast adaptation which involves two training steps. In the first step, we leverage hybrid contrastive learning in discriminant space and masked language modeling to make predictions at both sentence and token levels, which can enhance the model discriminability and representation learning ability respectively. In the second step, we design a pipeline for generating and filtering unseen data by only providing unseen intent labels, and utilize parameter-efficient fine-tuning to quickly adapt to unseen intents. Experiments on four intent detection datasets demonstrate that our two-step training method has better comprehension and generalization capabilities.

Multi-label document classification (MLDC) aims to allocate more than one label to each document and attracts increasing attention in many practical applications. However, previous studies have failed to pay sufficient attention to the lack of semantic information on labels and the long-tail problem prevalent in the datasets. Additionally, most existing methods focus on optimizing document features, overlooking the potential of high-quality label features to enhance classification performance. In this paper, we propose a simple and effective paradigm for MLDC. Regarding the problem of insufficient label information and imbalance in the sample size of categories, we utilize large language models (LLMs) to semantically expand the label content and generate pseudo-samples for the tail categories. To optimize the features of both documents and labels, we design the contrastive learning boosted feature optimization module facilitated by the similarity matrices. Finally, we construct a label-guided feature selection module to incorporate the optimized label features into the input features to provide richer semantic information for the classifier. Extensive experiments have demonstrated that our proposed method significantly outperforms state-of-the-art baselines.