Visual grounding (VG) is an important task in vision and language that involves understanding the mutual relationship between query terms and images. However, existing VG datasets typically use simple and intuitive textual descriptions, with limited attribute and spatial information between images and text. Recently, the Scene Knowledge Visual Grounding (SK-VG) task has been introduced, which constructs VG datasets using visual knowledge and relational referential expressions. Due to the length of textual visual knowledge and the complexity of the referential relationships between entities, previous models have struggled with this task. Therefore, we propose ReadVG, a zero-shot, plug-and-play method that leverages the robust language understanding capabilities of Large Language Models (LLMs) to transform long visual knowledge texts into concise, information-dense visual descriptions. To improve the accuracy of target localisation, we employ a multi-step parsing algorithm that can progressively extract the query targets and their features from the visual knowledge and relational referencing expressions, thereby assisting multimodal models to more accurately localise the target for grounding purposes. Extensive experiments and case studies show that our approach can significantly improve the performance of multimodal grounding models.

Large Language Models (LLMs) have demonstrated promising potential in providing empathetic support during interactions. However, their responses often become verbose or overly formulaic, failing to adequately address the diverse emotional support needs of real-world scenarios. To tackle this challenge, we propose an innovative strategy-enhanced role-playing framework, designed to simulate authentic emotional support conversations. Specifically, our approach unfolds in two steps: (1) Strategy-Enhanced Role-Playing Interactions, which involve three pivotal roles—Seeker, Strategy Counselor, and Supporter—engaging in diverse scenarios to emulate real-world interactions and promote a broader range of dialogues; and (2) Emotional Support Agent Training, achieved through fine-tuning LLMs using our specially constructed dataset. Within this framework, we develop the ServeForEmo dataset, comprising an extensive collection of 3.7K+ multi-turn dialogues and 62.8K+ utterances. We further present SweetieChat, an emotional support agent capable of handling diverse open-domain scenarios. Extensive experiments and human evaluations confirm the framework’s effectiveness in enhancing emotional support, highlighting its unique ability to provide more nuanced and tailored assistance.

Reinforcement Learning Fine-Tuning (RLFT) has achieved notable success in tasks with objectively verifiable answers (e.g., code generation, mathematical reasoning), yet struggles with open-ended subjective tasks like role-playing dialogue. Traditional reward modeling approaches, which rely on independent sample-wise scoring, face dual challenges: subjective evaluation criteria and unstable reward signals. Motivated by the insight that human evaluation inherently combines explicit criteria with implicit comparative judgments, we propose Comparative Policy Optimization (CPO). CPO redefines the reward evaluation paradigm by shifting from sample-wise scoring to comparative group-wise scoring. Building on the same principle, we introduce the CharacterArena evaluation framework, which comprises two stages: (1) Contextualized Multi-turn Role-playing Simulation, and (2) Trajectory-level Comparative Evaluation. By operationalizing subjective scoring via objective trajectory comparisons, CharacterArena minimizes contextual bias and enables more robust and fair performance evaluation. Empirical results on CharacterEval, CharacterBench, and CharacterArena confirm that CPO effectively mitigates reward ambiguity and leads to substantial improvements in dialogue quality.

Effective emotional support hinges on understanding users’ emotions and needs to provide meaningful comfort during multi-turn interactions. Large Language Models (LLMs) show great potential for expressing empathy; however, they often deliver generic responses that fail to address users’ specific needs. To tackle this issue, we propose a self-evolution framework designed to help LLMs improve their responses to better align with users’ implicit preferences concerning personality, emotional state, and specific context. Our framework consists of two distinct phases: (1) Emotional Support Experience Acquisition, where LLMs are fine-tuned on limited emotional support conversation data to provide basic support, and (2) Self-Improvement for Personalized Emotional Support, where LLMs leverage self-reflection and self-refinement to generate personalized responses. Through iterative direct preference optimization between the pre- and post-refined responses, our model generates responses that reflect a better understanding of the user’s implicit preferences. Extensive experiments and evaluations demonstrate that our method significantly enhances the model’s performance in emotional support, reducing unhelpful responses and minimizing discrepancies between user preferences and model outputs.

Self-attention and position embedding are two crucial modules in transformer-based Large Language Models (LLMs). However, the potential relationship between them is far from well studied, especially for long context window extending. In fact, anomalous behaviors that hinder long context extrapolation exist between Rotary Position Embedding (RoPE) and vanilla self-attention.Incorrect initial angles between Q and K can cause misestimation in modeling rotary position embedding of the closest tokens.To address this issue, we propose Collinear Constrained Attention mechanism, namely CoCA. Specifically, we enforce a collinear constraint between Q and K to seamlessly integrate RoPE and self-attention.While only adding minimal computational and spatial complexity, this integration significantly enhances long context window extrapolation ability. We provide an optimized implementation, making it a drop-in replacement for any existing transformer-based models.Extensive experiments demonstrate that CoCA excels in extending context windows. A CoCA-based GPT model, trained with a context length of 512, can extend the context window up to 32K (60×) without any fine-tuning.Additionally, incorporating CoCA into LLaMA-7B achieves extrapolation up to 32K within a training length of only 2K.Our code is publicly available at: https://github.com/codefuse-ai/Collinear-Constrained-Attention

Decoding continuous language from brain activity is a formidable yet promising field of research. It is particularly significant for aiding people with speech disabilities to communicate through brain signals. This field addresses the complex task of mapping brain signals to text. The previous best attempt reverse-engineered this process in an indirect way: it began by learning to encode brain activity from text and then guided text generation by aligning with predicted brain responses. In contrast, we propose a simple yet effective method that guides text reconstruction by directly comparing them with the predicted text embeddings mapped from brain activities. Comprehensive experiments reveal that our method significantly outperforms the current state-of-the-art model, showing average improvements of 77% and 54% on BLEU and METEOR scores. We further validate the proposed modules through detailed ablation studies and case analyses and highlight a critical correlation: the more precisely we map brain activities to text embeddings, the better the text reconstruction results. Such insight can simplify the task of reconstructing language from brain activities for future work, emphasizing the importance of improving brain-to-text-embedding mapping techniques.

Large language models (LLMs) have demonstrated exceptional performance in reasoning tasks with dedicated Chain-of-Thought (CoT) prompts. Further enhancing CoT prompts with exquisite exemplars can significantly improve reasoning performance.However, the effectiveness of CoT prompts may fluctuate dramatically with different choices of in-context examples. Additionally, manual construction of rationale steps can be time-consuming, presenting challenges for the widespread adoption of CoT prompting. In this work, we propose a novel approach by introducing information entropy (IE) as a criteria on for CoT prompt selection. We extend this criterion to the CoT generation and inference stages, automatically generating CoT prompts with higher information entropy scores and adaptively determining the number of samples. These three stages together form our proposed information- entropy-based multi-step reasoning for large language models, named INFORM. Our experiments across seven reasoning benchmarks utilizing two language models(GPT-3.5-Turbo and text-davinci-003) demonstrate the superiority of INFORM both in performance and efficiency.

自然语言文本中存在大量否定语义表达,否定焦点识别任务作为更细粒度的否定语义分析,近年来开始受到自然语言处理学者的关注。该任务旨在识别句子中被否定词修饰和强调的文本片段,其对自然语言处理的下游任务,如情感分析、观点挖掘等具有重要意义。与英语相比,目前面向汉语的否定焦点识别研究彶展缓慢,其主要原因是尚未有中文数据集为模型提供训练和测试数据。为解决上述问题,本文在汉语否定与不确定语料库上进行了否定焦点的标注工作,初步探索了否定焦点在汉语上的语言现象,并构建了一个包含5,762个样本的数据集。同时,本文还提出了一个基于神经网络模型的基线系统,为后续相关研究提供参照。