When large language models are used in real-world scenarios, continual learning (CL) becomes a non-trivial problem. In particular, continual learning with modern LLMs is challenged both by the substantial computational costs induced by their massive parameter scale, and by the limitations of current CL methods, which are mainly designed to mitigate catastrophic forgetting while neglecting knowledge sharing across tasks. We further observe that models with stronger performance exhibit stronger inter-task connections. In light of the above challenges and findings, we propose Attribution Scores-based Soft Orthogonality Low-Rank Adaptation (ASO-LoRA), an effective and efficient framework that simultaneously facilitates knowledge transfer while mitigating catastrophic forgetting. Specifically, ASO-LoRA initially assigns task-specific parameter subspaces for new tasks utilizing multi-LoRA modules, enabling for efficient training and inference without relying on task labels. Then, ASO-LoRA leverages attribution scores to evaluate task similarity and employs soft orthogonality between task-specific subspaces, guiding gradient updates in directions that promote parameter isolation, achieving a balance between knowledge transfer and preservation. Experiments are carried out on both the T5-large and the LLaMA2-7B, showing ASO-LoRA’s superior performance and suitability as a plug-in CL solution for general Transformer-based LLMs. Code is available at https://github.com/736619821/ASO-LORA.

Curiosity serves as a fundamental construct in human cognition.Inspired by curiosity, reinforcement learning with intrinsic rewards for large language models (LLMs) has shown substantial potential.However, it remains unclear whether existing curiosity-driven methods genuinely reflect curiosity-like behaviors in LLMs, and to what extent psychological notions of curiosity can be transferred to these models. In this work, we propose a psychology-inspired framework to evaluate and leverage curiosity in LLMs.We adapt the Five-Dimensional Curiosity scale Revised (5DCR) to LLMs and combine questionnaire-based self reports with behavioral study.We find that although LLMs can exhibit curiosity-like behavioral patterns resembling those of humans, such patterns do not reflect an intrinsic trait of curiosity.Building on this insight, we design a curiosity-driven thinking pipeline to examine the functional role of human-like curious behaviors. Experiments show that instructing LLMs to emulate curious strategies leads to better performance on selected downstream tasks, indicating that mimicking curious behaviors holds promise for reasoning enhancement.

Large Language Model (LLMs) can be used to write or modify documents, presenting a challenge for understanding the intent behind their use. For example, benign uses may involve using LLM on a human-written document to improve its grammar or to translate it into another language. However, a document entirely produced by a LLM may be more likely to be used to spread misinformation than simple translation (, from use by malicious actors or simply by hallucinating). Prior works in Machine Generated Text (MGT) detection mostly focus on simply identifying whether a document was human or machine written, ignoring these fine-grained uses. In this paper, we introduce a HiErarchical, length-RObust machine-influenced text detector (HERO), which learns to separate text samples of varying lengths from four primary types: human-written, machine-generated, machine-polished, and machine-translated. HERO accomplishes this by combining predictions from length-specialist models that have been trained with Subcategory Guidance. Specifically, for categories that are easily confused (, different source languages), our Subcategory Guidance module encourages separation of the fine-grained categories, boosting performance. Extensive experiments across five LLMs and six domains demonstrate the benefits of our HERO, outperforming the state-of-the-art by 2.5-3 mAP on average.

Fine-grained address entity recognition (FGAER) from multi-turn spoken dialogues is particularly challenging. The major reason lies in that a full address is often formed through a conversation process. Different parts of an address are distributed through multiple turns of a dialogue with spoken noises. It is nontrivial to extract by turn and combine them. This challenge has not been well emphasized by main-stream entity extraction algorithms. To address this issue, we propose in this paper a logic-guided fine-grained address recognition method (Log-FGAER), where we formulate the address hierarchy relationship as the logic rule and softly apply it in a probabilistic manner to improve the accuracy of FGAER. In addition, we provide an ontology-based data augmentation methodology that employs ChatGPT to augment a spoken dialogue dataset with labeled address entities. Experiments are conducted using datasets generated by the proposed data augmentation technique and derived from real-world scenarios. The results of the experiment demonstrate the efficacy of our proposal.