Aspect-based sentiment triplet extraction tasks remain a long-standing challenge, which aim to achieve aspect, opinion, and sentiment polarity from sentences. Most existing methods achieve excellent performance by exploring the interactions between aspect and opinion terms. However, few studies focus on the positive impact of sentiment on triplet extraction. As sentiment acts as a key cue in triplet extraction, its role is often overlooked, thereby limiting extraction performance. This paper proposes a novel framework, duality-driven aspect sentiment triplet extraction with a large language model and iterative reinforcement, which integrates duality-driven with a large language model for the aspect sentiment triplet task. This study employs a duality-driven strategy based on symmetry to extract aspect-based sentiment triplets, fully taking into account sentiment polarity during the interaction between aspects and opinions. Moreover, this study devises a two-view prompt template for prior knowledge fusion based on large language models and employs confidence cycle iteration strategies to alleviate cascading errors. Extensive experiments show that the framework outperforms the previous state-of-the-art model. These findings demonstrate that the proposed model makes a positive impact on the aspect sentiment triplet extraction task overall.

Humans often express empathy toward others in daily conversations, and exploring ways to respond empathetically is a crucial step in building human-like dialogue systems. Psychological theories hold that empathy mainly consists of two aspects: affection and cognition. However, previous approaches fail to adequately integrate these two aspects, which could lead to inappropriate responses. To address this issue, we propose a reinforcement learning method that integrates empathy level alignment and semantic relevance (RLES) for empathetic response generation. RLES is primarily divided into two stages: fine-tuning the pre-trained T5 model during the supervised learning stage, and then further training the fine-tuned T5 model during the reinforcement learning stage. Specifically, during the supervised learning stage, we fine-tune the pre-trained T5 model to improve its adaptability in empathetic dialogue scenarios. During the reinforcement learning stage, we first initialize the policy with the fine-tuned T5 model. Then, a reward model was designed by incorporating empathy level alignment and semantic relevance from the perspectives of affective and cognitive empathy, respectively. This design reflects a balanced symmetry between the affective and cognitive aspects of empathy in their contributions to the final reward. Finally, the PPO algorithm was used to further train the fine-tuned T5 model, enabling it to generate empathetic responses by maximizing the expected reward. Extensive experimental results on a benchmark dataset demonstrate that RLES outperforms the baselines in both automatic and human evaluations and can generate more empathetic and semantically relevant responses.

Natural language-guided drone geo-localization (DGL) provides an intuitive and scalable mode of human-drone interaction for tasks such as search, rescue, and surveillance. Recent Vision-Language Models (VLMs) can learn semantic correspondences between text and images during fine-tuning. However, their performance in DGL tasks remains constrained, as complex instructions and cluttered scenes often cause semantic dilution and granularity mismatch, leading to weak cross-modal alignment. Consequently, the models struggle with ambiguous targets and suffer from reduced localization accuracy. To address these challenges, we propose SAA-DGL, a framework for interpretable language-guided Drone Geo-Localization that enriches Semantic Attribute Alignment (SAA) with large language models (LLMs). It introduces two parameter-free cross-modal fusion modules: (1) the LLM-driven Cross-modal Semantic Attribute Enrichment (LCSAE) module, which extracts fine-grained attributes (e.g., color, shape, position) from text and embeds them into visual features as explicit semantic anchors, producing semantically enriched cross-modal representations; and (2) the Bidirectional Feature Alignment (BFA) module, which builds fusion relationships between visual and textual features via similarity-driven mechanisms, enabling effective integration of enriched visual and textual information. This design improves cross-modal consistency and interpretability while preserving pretrained alignment priors and enhancing training stability. Experiments on the GeoText-1652 benchmark show that SAA-DGL achieves state-of-the-art performance and strong robustness under complex visual and linguistic disturbances, validating its effectiveness for challenging geo-localization scenarios. We will release the code.