Multimodal metaphor detection is an important and challenging task in multimedia computing, which aims to distinguish between metaphorical and literal multimodal expressions. Existing studies mainly utilize typical multimodal computing approaches for detection, neglecting the unique cross-domain and cross-modality characteristics underlying multimodal metaphor understanding. According to Conceptual Metaphor Theory (CMT), the inconsistency between source and target domains and their attribute similarity are essential to infer the intricate meanings implied in metaphors. In practice, the scarcity of the annotated multimodal metaphorical contents in the real world brings additional difficulty to the detection task and further complicates the understanding of multimodal metaphors. To address the above challenges, in this paper, we propose a novel Imaginative FRame Augmented (ImaRA) method for low-resource multimodal metaphor detection and explanation inspired by CMT. Specifically, we first identify imaginative frame as an associative structure to stimulate the imaginative thinking of multimodal metaphor detection and understanding. We then construct a cross-modal imagination dataset rich in multimodal metaphors and corresponding imaginative frames, and retrieve an augmented instance from this imagination dataset using imaginative frames mined from the input. This augmented instance serves as the demonstration exemplar to boost the metaphor reasoning ability of the multimodal large language model (MLLM) in low-resource multimodal scenarios. Experiments on two publicly available datasets show that our method consistently achieves robust results compared to MLLM-based methods for both multimodal metaphor detection and explanation in low-resource scenarios and meanwhile surpasses existing multimodal metaphor detection methods with full training data.

Large language models (LLMs) enabled dialogue systems have become one of the central modes in human-machine interaction, which bring about vast amounts of conversation logs and increasing demand for dialogue generation. The dialogue’s life-cycle spans from Prelude through Interlocution to Epilogue, encompassing rich dialogue elements. Despite large volumes of dialogue-related studies, there is a lack of systematic investigation into the dialogue stages to frame benchmark construction that covers comprehensive dialogue elements. This hinders the precise modeling, generation and assessment of LLMs-based dialogue systems. To bridge this gap, in this paper, we introduce a new research task—Dialogue Element MOdeling, including Element Awareness and Dialogue Agent Interaction, and propose a novel benchmark, DEMO, designed for a comprehensive dialogue modeling and assessment. On this basis, we further build the DEMO agent with the adept ability to model dialogue elements via imitation learning. Extensive experiments on DEMO indicate that current representative LLMs still have considerable potential for enhancement, and our DEMO agent performs well in both dialogue element modeling and out-of-domain tasks.

The development of Multimodal Large Language Models (MLLMs) has seen significant progress, driven by increasing demands across various fields (e.g., multimodal agents, embodied intelligence). While model-driven approaches aim to enhance MLLM capabilities through diverse architectures, their performance gains have become increasingly marginal. In contrast, data-driven methods, which scale up image-text instruction datasets, have proven more effective but face challenges related to limited data diversity and complexity. The absence of high-quality instruction data remains a major bottleneck in MLLM development. To address this issue, we propose , a novel multimodal instruction data evolution framework. This framework iteratively enhances data quality through a refined combination of fine-grained perception, cognitive reasoning, and interaction evolution, generating a more complex and diverse image-text instruction dataset that significantly improves MLLM capabilities. Starting with an initial dataset, SEED-163K, we employ to systematically expand instruction diversity, extend visual reasoning steps to improve cognitive abilities, and extract fine-grained visual details to enhance understanding and robustness. To rigorously evaluate our approach, we conduct extensive qualitative analysis and quantitative experiments across 13 vision-language tasks. Compared to baseline models trained on the original seed dataset, our method achieves an average accuracy improvement of 3.1 percentage points. Moreover, our approach attains state-of-the-art (SOTA) performance in nine tasks while using significantly less data than existing state-of-the-art models.

Long-context modeling capabilities of Large Language Models (LLMs) have garnered widespread attention, leading to the emergence of LLMs with ultra-context windows. Meanwhile, benchmarks for evaluating long-context language models are gradually catching up. However, existing benchmarks employ irrelevant noise texts to artificially extend the length of test cases, diverging from the real-world scenarios of long-context applications. To bridge this gap, we propose a novel long-context benchmark, Loong, aligning with realistic scenarios through extended multi-document question answering (QA). Unlike typical document QA, in Loong’s test cases, each document is relevant to the final answer, ignoring any document will lead to the failure of the answer. Furthermore, Loong introduces four types of tasks with a range of context lengths: Spotlight Locating, Comparison, Clustering, and Chain of Reasoning, to facilitate a more realistic and comprehensive evaluation of long-context understanding. Extensive experiments indicate that existing long-context language models still exhibit considerable potential for enhancement. Retrieval augmented generation (RAG) achieves poor performance, demonstrating that Loong can reliably assess the model’s long-context modeling capabilities.

The development of large language models (LLMs) raises the importance of assessing the fairness and completeness of various evaluation benchmarks. Regrettably, these benchmarks predominantly utilize uniform manual prompts, which may not fully capture the expansive capabilities of LLMs—potentially leading to an underestimation of their performance. To unlock the potential of LLMs, researchers pay attention to automated prompt search methods, which employ LLMs as optimizers to discover optimal prompts. However, previous methods generate the solutions implicitly, which overlook the underlying thought process and lack explicit feedback. In this paper, we propose a novel prompt introspective search framework, namely PromISe, to better release the capabilities of LLMs. It converts the process of optimizing prompts into an explicit chain of thought, through a step-by-step procedure that integrates self-introspect and self-refine. Extensive experiments, conducted over 73 tasks on two major benchmarks, demonstrate that our proposed PromISe significantly boosts the performance of 12 well-known LLMs compared to the baseline approach. Moreover, our study offers enhanced insights into the interaction between humans and LLMs, potentially serving as a foundation for future designs and implementations. Keywords: large language models, prompt search, self-introspect, self-refine