Abstract Stance detection on social media plays a vital role in understanding public opinion on contentious topics. While prior work leverages external knowledge sources like Wikipedia to enrich limited target information, it primarily introduces conceptual content, neglecting the interpretability potential of knowledge and often leading to the incorporation of irrelevant or redundant information that hinders stance prediction performance. To address this, we introduce PiKGL, a Pruned interpretable Knowledge Graph Learning framework for explainable stance detection. Specifically, we first extract event triplets and topics to obtain real-world knowledge, which is then used to construct an interpretable knowledge graph. To ensure precision and minimize noise, we introduce a retrieval-guided pruning strategy that incorporates commonsense knowledge, filtering redundant information of the interpretable knowledge graph. Finally, the pruned knowledge graph is injected into a large language model to jointly model textual, target, and commonsense for improved stance comprehension. Experimental results conducted on three public datasets demonstrate our PiKGL achieves state-of-the-art performance on stance detection.

Stickers are widely used in online chatting, which can vividly express someone's intention, emotion, or attitude. Existing conversation research typically retrieves stickers based on a single session or the previous textual information, which can not adapt to the multi-modal and multi-session nature of the real-world conversation. To this end, we introduce MultiChat, a new dataset for sticker retrieval facing the multi-modal and multi-session conversation, comprising 1,542 sessions, featuring 50,192 utterances and 2,182 stickers. Based on the created dataset, we propose a novel Intent-Guided Sticker Retrieval (IGSR) framework that retrieves stickers for multi-modal and multi-session conversation history drawing support from intent learning. Specifically, we introduce sticker attributes to better leverage the sticker information in multi-modal conversation, which are incorporated with utterances to construct a memory bank. Further, we extract relevant memories for the current conversation from the memory bank to identify the intent of the current conversation, and then retrieve a sticker to respond guided by the intent. Extensive experiments on our MultiChat dataset reveal the robustness and effectiveness of our IGSR approach in multi-session, multi-modal scenarios.

Image multi-emotion prediction aims to identify the emotions evoked by images in humans. In the real world, individual cognitive differences can lead to different viewers experiencing varied emotions. Most existing researchers primarily focus on analyzing image features, which are limited to the perceptual level, leading to a superficial understanding of emotions. To address this gap, we propose an Emotional Reasoning Chain (EReC) based on a multimodal large language model, which learns both perception and reasoning abilities for multi-emotion prediction. Specifically, we design a parameter-efficient fine-tuning paradigm encompassing three task instructions: perception, reasoning, and prediction instruction. This paradigm involves a progressive process to perform targeted instructions within a domain for fine-tuning, thereby optimizing the model’s capabilities in image perception, cognitive reasoning, and multi-emotion prediction. Furthermore, to alleviate hallucinations of large models, a Reason-level Alignment Score (RAS) is introduced to guide the model toward closer alignment with human cognition. Experiments on four datasets demonstrate that our EReC method, which emulates the human cognitive process of viewing and interpreting images, employs progressive tuning to integrate the perceptual, reasoning, and predictive capabilities of large models, achieving superior performance.

As Large Language Models (LLMs) have been increasingly deployed for various language generation tasks, ensuring the safety and appropriateness of generated content has become a critical challenge. While these models excel at tasks such as dialogue generation, text completion, and content creation, they may inadvertently generate harmful or inappropriate responses that compromise user safety and model reliability. Recent advancements have attempted to address this problem by incorporating safety constraints within the Reinforcement Learning with Human Feedback (RLHF) framework. However, these approaches struggle with the complexity of selecting appropriate compromise parameters, leading to suboptimal performance in safe language generation. To address this problem, we explore advanced alternative first-order constrained optimization strategies that dynamically adjust the balance of helpfulness and harmlessness during training. Specifically, we choose FOCOPS and P3O, which have demonstrated strong performance in various reinforcement learning tasks, and integrate them into the RLHF framework to balance the quality and safety of generated responses. Then we evaluate the performance of different constrained optimization methods for language generation. The results indicate that the P3O algorithm shows significant potential to advance safe language generation while maintaining the helpfulness of model responses.

Conversational stance detection, which aims to identify stances within conversation threads, has become a research hotspot recently. As the number of dialogue turns increases and the conversation content becomes more complex, existing methods that simply incorporate conversational context are insufficient to effectively capture the nuanced information necessary for accurate stance detection. To address this issue, we introduce a Multi-agent Reasoning Graph Knowledge Distillation (MRGKD) framework, leveraging conversational reasoning among multiple Large Language Models (LLMs) into smaller language models. Specifically, we first construct a multi-agent reasoning graph to infer implicit logical relationships within the conversational history from the diverse perspectives of multiple LLMs. To fully leverage the in-context learning capabilities of LLMs, we design a reasoning knowledge editing mechanism that internalizes new information by aligning the output distribution of smaller language models with both the conversational history and the knowledge derived from the multi-agent reasoning graph. Additionally, we incorporate a contrastive loss to distinguish between correct and incorrect reasoning, alongside a stance detection loss, to fine-tune the smaller language models. This approach not only ensures the accurate acquisition of logical knowledge but also preserves the integrity of the conversational history. Experiments conducted on two public datasets demonstrate that our MRGKD significantly outperforms all baselines.