Zhen Qi

Zhen Qi, Jiajing Chen, Shuo Wang et al.

This study aims to explore the performance improvement method of large language models based on GPT-4 under the multi-task learning framework and conducts experiments on two tasks: text classification and automatic summary generation. Through the combined design of shared feature extractors and task-specific modules, we achieve knowledge-sharing and optimization of multiple tasks in the same model. The experiment uses multiple subtasks of the GLUE dataset to compare the performance of the multi-task model with the single-task GPT-4, the multi-task version of GPT-3, the BERT basic model, and the classic Bi-LSTM with Attention model. The results show that the proposed multi-task learning model outperforms other comparison models in terms of text classification accuracy and ROUGE value of summary generation, demonstrating the advantages of multi-task learning in improving model generalization ability and collaborative learning between tasks. The model maintains a stable loss convergence rate during training, showing good learning efficiency and adaptability to the test set. This study verifies the applicability of the multi-task learning framework in large language models, especially in improving the model's ability to balance different tasks. In the future, with the combination of large language models and multimodal data and the application of dynamic task adjustment technology, the framework based on multi-task learning is expected to play a greater role in practical applications across fields and provide new ideas for the development of general artificial intelligence.

Jiacheng Hu, Xiaoxuan Liao, Jia Gao et al.

This study proposes a large language model optimization method based on the improved LoRA fine-tuning algorithm, aiming to improve the accuracy and computational efficiency of the model in natural language processing tasks. We fine-tune the large language model through a low-rank adaptation strategy, which significantly reduces the consumption of computing resources while maintaining the powerful capabilities of the pre-trained model. The experiment uses the QQP task as the evaluation scenario. The results show that the improved LoRA algorithm shows significant improvements in accuracy, F1 score, and MCC compared with traditional models such as BERT, Roberta, T5, and GPT-4. In particular, in terms of F1 score and MCC, our model shows stronger robustness and discrimination ability, which proves the potential of the improved LoRA algorithm in fine-tuning large-scale pre-trained models. In addition, this paper also discusses the application prospects of the improved LoRA algorithm in other natural language processing tasks, emphasizing its advantages in multi-task learning and scenarios with limited computing resources. Future research can further optimize the LoRA fine-tuning strategy and expand its application in larger-scale pre-trained models to improve the generalization ability and task adaptability of the model.

Zhou Fang, Hanlu Zhang, Jacky He et al.

This study aims to develop an efficient and accurate model for detecting malicious comments, addressing the increasingly severe issue of false and harmful content on social media platforms. We propose a deep learning model that combines BERT and BiLSTM. The BERT model, through pre-training, captures deep semantic features of text, while the BiLSTM network excels at processing sequential data and can further model the contextual dependencies of text. Experimental results on the Jigsaw Unintended Bias in Toxicity Classification dataset demonstrate that the BERT+BiLSTM model achieves superior performance in malicious comment detection tasks, with a precision of 0.94, recall of 0.93, and accuracy of 0.94. This surpasses other models, including standalone BERT, TextCNN, TextRNN, and traditional machine learning algorithms using TF-IDF features. These results confirm the superiority of the BERT+BiLSTM model in handling imbalanced data and capturing deep semantic features of malicious comments, providing an effective technical means for social media content moderation and online environment purification.

Guangliang Liu, Zimo Qi, Xitong Zhang et al.

Ensuring that Large Language Models (LLMs) return just responses which adhere to societal values is crucial for their broader application. Prior research has shown that LLMs often fail to perform satisfactorily on tasks requiring moral cognizance, such as ethics-based judgments. While current approaches have focused on fine-tuning LLMs with curated datasets to improve their capabilities on such tasks, choosing the optimal learning paradigm to enhance the ethical responses of LLMs remains an open research debate. In this work, we aim to address this fundamental question: can current learning paradigms enable LLMs to acquire sufficient moral reasoning capabilities? Drawing from distributional semantics theory and the pragmatic nature of moral discourse, our analysis indicates that performance improvements follow a mechanism similar to that of semantic-level tasks, and therefore remain affected by the pragmatic nature of morals latent in discourse, a phenomenon we name the pragmatic dilemma. We conclude that this pragmatic dilemma imposes significant limitations on the generalization ability of current learning paradigms, making it the primary bottleneck for moral reasoning acquisition in LLMs.

Shuo Wang, Chihang Wang, Jia Gao et al.

This study proposes a knowledge distillation algorithm based on large language models and feature alignment, aiming to effectively transfer the knowledge of large pre-trained models into lightweight student models, thereby reducing computational costs while maintaining high model performance. Different from the traditional soft label distillation method, this method introduces a multi-layer feature alignment strategy to deeply align the intermediate features and attention mechanisms of the teacher model and the student model, maximally retaining the semantic expression ability and context modeling ability of the teacher model. In terms of method design, a multi-task loss function is constructed, including feature matching loss, attention alignment loss, and output distribution matching loss, to ensure multi-level information transfer through joint optimization. The experiments were comprehensively evaluated on the GLUE data set and various natural language processing tasks. The results show that the proposed model performs very close to the state-of-the-art GPT-4 model in terms of evaluation indicators such as perplexity, BLEU, ROUGE, and CER. At the same time, it far exceeds baseline models such as DeBERTa, XLNet, and GPT-3, showing significant performance improvements and computing efficiency advantages. Research results show that the feature alignment distillation strategy is an effective model compression method that can significantly reduce computational overhead and storage requirements while maintaining model capabilities. Future research can be further expanded in the directions of self-supervised learning, cross-modal feature alignment, and multi-task transfer learning to provide more flexible and efficient solutions for the deployment and optimization of deep learning models.

Guangliang Liu, Zimo Qi, Xitong Zhang et al.

Although there has been growing interest in the self-correction capability of Large Language Models (LLMs), there are varying conclusions about its effectiveness. Prior research has largely concentrated on intrinsic self-correction, extrinsic self-correction, particularly the interplay between internal knowledge and external feedback, remains underexplored. In this paper, we aim to comprehensively investigate the underlying mechanism of moral self-correction by addressing a fundamental question: is moral self-correction an innate capability of LLMs? Specifically, we conduct: (1) a behavioral analysis of LLMs' moral sensitivity based on a self-distinguishing task; and (2) a mechanistic analysis of the hidden states to examine how key components of self-correction, such as Chain-of-Thought (CoT) and external feedback, interact to facilitate moral self-correction. Drawing on empirical evidence from both behavioral and mechanistic analyses, we demonstrate that moral self-correction is not an inherent capability of LLMs, as they are neither morally sensitive nor able to effectively incorporate external feedback during the self-correction process.

Guangliang Liu, Zimo Qi, Xitong Zhang et al.

Moral self-correction has emerged as a promising approach for aligning the output of Large Language Models (LLMs) with human moral values. However, moral self-correction techniques are subject to two primary paradoxes. First, despite empirical and theoretical evidence to support the effectiveness of self-correction, this LLM capability only operates at a superficial level. Second, while LLMs possess the capability of self-diagnosing immoral aspects of their output, they struggle to identify the cause of this moral inconsistency during their self-correction process. To better understand and address these paradoxes, we analyze the discourse constructions in fine-tuning corpora designed to enhance moral self-correction, uncovering the existence of the heuristics underlying effective constructions. We demonstrate that moral self-correction relies on discourse constructions that reflect heuristic shortcuts, and that the presence of these heuristic shortcuts during self-correction leads to inconsistency when attempting to enhance both self-correction and self-diagnosis capabilities jointly. Based on our findings, we propose a solution to improve moral self-correction by leveraging the heuristics of curated datasets. We also highlight the generalization challenges of this capability, particularly in terms of learning from situated context and model scales.