Large Language Models (LLMs) as agents require careful behavioral adaptation. While adept at reactive tasks (e.g., medical reasoning), LLMs often struggle with proactive engagement, like unprompted identification of critical missing information or risks. We introduce **BehaviorBench**, a comprehensive dataset to evaluate agent behaviors across a clinical assistance spectrum. To rigorously test the current models, we also introduce **BehaviorBench-Hard**, a challenging subset where the performance of state-of-the-art models drops significantly, revealing weaknesses. To address these challenges, we propose **BehaviorSFT**, a novel training strategy using behavioral tokens to explicitly condition LLMs for dynamic behavioral selection which boosts performance on both benchmarks. Crucially, a blind clinician evaluation confirmed that our trained agents exhibit more realistic clinical behavior, striking a superior balance between helpful proactivity and necessary restraint versus standard fine-tuning or explicitly instructed agents. Project Page: https://behavior-adaptation.github.io/

“The purpose of Aspect Sentiment Triplet Extraction (ASTE) is to extract a triplet, including thetarget or aspect, its associated sentiment, and related opinion terms that explain the underlyingcause of the sentiment. Some recent studies fail to capture the strong interdependence betweenATE and OTE, while others fail to effectively introduce the relationship between aspects andopinions into sentiment classification tasks. To solve these problems, we construct a multi-roundmachine reading comprehension framework based on a rethink mechanism to solve ASTE tasksefficiently. The rethink mechanism allows the framework to model complex relationships be-tween entities, and exclusive classifiers and probability generation algorithms can reduce queryconflicts and unilateral drops in probability. Besides, the multi-round structure can fuse explicitsemantic information flow between aspect, opinion and sentiment. Extensive experiments showthat the proposed model achieves the most advanced effect and can be effectively applied toASTE tasks.”

“Multimodal Named Entity Recognition (MNER) is a challenging task in social mediadue to the combination of text and image features. Previous MNER work has focused onpredicting entity information after fusing visual and text features. However, pre-traininglanguage models have already acquired vast amounts of knowledge during their pre-training process. To leverage this knowledge, we propose a prompt network for MNERtasks (P-MNER).To minimize the noise generated by irrelevant areas in the image, wedesign a visual feature extraction model (FRR) based on FasterRCNN and ResNet, whichuses fine-grained visual features to assist MNER tasks. Moreover, we introduce a textcorrection fusion module (TCFM) into the model to address visual bias during modalfusion. We employ the idea of a residual network to modify the fused features using theoriginal text features. Our experiments on two benchmark datasets demonstrate that ourproposed model outperforms existing MNER methods. P-MNER’s ability to leveragepre-training knowledge from language models, incorporate fine-grained visual features,and correct for visual bias, makes it a promising approach for multimodal named entityrecognition in social media posts.”