Aligning large language models (LLMs) with human preferences is challenging due to the inherent heterogeneity and multi-dimensionality of these preferences. Existing inference-time multi-objective alignment methods often rely on fixed weights to combine preference models. However, different tokens contribute unequally to preference objectives during decoding. Ignoring such token-level variation may cause the loss of preference information and lead to objective interference. To alleviate this, we utilize the implicit rewards of preference models to estimate importance differences between tokens, and propose a new training-free and reward-free framework TAW to dynamically balance multiple objectives for inference-time alignment. We design the relative entropy and marginal entropy estimators to quantify the informativeness and uncertainty of preference objectives. TAW with the estimator ensures that more informative or reliable preferences are emphasized, while weak or uncertain signals are suppressed. Experiments on two LLM alignment benchmarks show that TAW outperforms strong baselines without predefined weightings, while also achieves an overall optimal Pareto front when explicit preference weighting is applied.

Traditional psychological counseling struggles to meet public demand due to high costs, social stigma, and limited accessibility. Recently, large language models (LLMs) have shown great potential in healthcare, offering new opportunities to build accessible mental health dialogue systems. However, current LLMs often lack accurate modeling of cognitive empathy, especially the ability to understand users’ emotions and their underlying psychological causes. To address this, we propose CogEmp, a dialogue generation model tailored for the Chinese cultural context that integrates cognitive empathy. The model follows a three-stage decision pipeline: emotion and cause recognition, contextual understanding, and empathetic response generation. First, the model identifies the user’s fine-grained emotions and their underlying causes within the Chinese context, laying the foundation for personalized emotional comprehension. Then, it retrieves semantically similar counseling cases to extract topic and strategy information, thereby constructing a context-aware representation. Finally, guided by the extracted multi-dimensional cues, the model drives LLMs to generate empathetic responses that are both contextually appropriate and professionally grounded. Experiments conducted on Chinese mental health datasets show that CogEmp outperforms existing approaches in key evaluation metrics, particularly in empathy, comprehensibility, and professionalism.

Multi-task learning (MTL) enables joint learning over multiple tasks based on shared representations, but suffers from task interference issue during optimization. Existing works mainly focus on task balancing or probabilistic modeling but fail to address the issue since they struggle to learn sufficient representations for all target tasks. To address this, we propose a multi-task representation alignment (MTRA) framework to achieve task-specific alignment and self-alignment on the shared representations from a mutual information perspective. MTRA ensures that the learned representations contain task-relevant features while mitigating the negative effects of task-irrelevant features. First, we design a task-specific alignment objective to align the shared representations and task-specific representations with the expected targets of all tasks via information maximization. Besides, we design a self-alignment objective to eliminate task-irrelevant features via conditional information minimization. Experiments on two multi-task language benchmarks show that MTRA outperforms 13 representative MTL methods under the same settings, particularly under label-noisy and data-constrained conditions. Further analysis shows that the learned shared representations exhibit sufficient task informativeness and superior alignment properties.