11 accepted papers
Large language models (LLMs) excel at logical and algorithmic reasoning, yet their emotional intelligence (EQ) still lags far behind their cognitive prowess. While reinforcement learning from verifiable rewards (RLVR) has advanced in other domains, its application to dialogue—especially for emotion…
Reasoning based on chains of thought (CoTs) enables large language models (LLMs) to solve problems by thinking step by step and becomes the mainstream solution for Question-Answering (QA) tasks. Knowledge graph (KG)-enhanced CoT technology helps correct factual errors or predict reasoning direction.
Mathematical reasoning ability objectively reflects a language model's understanding of implicit knowledge in contexts, with logic being a prerequisite for exploring, articulating and establishing effective reasoning. Large language models (LLMs) have shown great potential in complex reasoning tasks…
Weakly supervised learning aims to make machine learning more powerful when the perfect supervision is unavailable, and has attracted much attention from researchers. Among the various scenarios of weak supervision, one of the most challenging cases is learning from multiple unlabeled (U) datasets w…
Abductive Learning (ABL) is a promising framework for integrating sub-symbolic perception and logical reasoning through abduction. In this case, the abduction process provides supervision for the perception model from the background knowledge. Nevertheless, this process naturally contains uncertaint…
Meta learning is a promising paradigm in the era of large models and task distributional robustness has become an indispensable consideration in real-world scenarios. Recent advances have examined the effectiveness of tail task risk minimization in fast adaptation robustness improvement \citep{wang…
Meta learning is a promising paradigm to enable skill transfer across tasks. Most previous methods employ the empirical risk minimization principle in optimization. However, the resulting worst fast adaptation to a subset of tasks can be catastrophic in risk-sensitive scenarios. To robustify fast ad…
Discriminability and transferability are two goals of feature learning for domain adaptation (DA), as we aim to find the transferable features from the source domain that are helpful for discriminating the class label in the target domain. Modern DA approaches optimize discriminability and transfera…
In this paper, we address a special scenario of semi-supervised learning, where the label missing is caused by a preceding filtering mechanism, i.e., an instance can enter a subsequent process in which its label is revealed if and only if it passes the filtering mechanism. The rejected instances are…
Conditional sentence generation aims to generate proper target sentences with the given condition, and has shown great promise in many text generation applications such as dialogue systems and poetry generation. The ensemble of retrieval and generation-based models retrieve texts according to the in…
Network quantization is an effective method for network compression. Existing methods train a low-precision network by fine-tuning from a pre-trained model. However, training a low-precision network often requires large-scale labeled data to achieve superior performance. In many real-world scenarios…