Current research on long-form context in Large Language Models (LLMs) primarily focuses on the understanding of long-contexts, the Openended Long Text Generation (Open-LTG) remains insufficiently explored.Training a longcontext generation model requires curation of gold-standard reference data, which is typically nonexistent for informative Open-LTG tasks.However, previous methods only utilize general assessments as reward signals, which limits accuracy.To bridge this gap, we introduce ProxyReward, an innovative reinforcement learning (RL) based framework, which includes a dataset and a reward signal computation method.Firstly, ProxyReward Dataset generation is accomplished through simple prompts that enables the model to create automatically, obviating extensive labeled data or significant manual effort.Secondly, ProxyReward Signal offers a targeted evaluation of information comprehensiveness and accuracy for specific questions.The experimental results indicate that our method Prox-yReward surpasses even GPT-4-Turbo.It can significantly enhance performance by 20% on the Open-LTG task when training widely used open-source models, while also surpassing the LLM-as-a-Judge approach.Our work presents effective methods to enhance the ability of LLMs to address complex open-ended questions posed by humans.

The development of social media (e.g., Twitter) allows users to make speeches with low cost and broad influence. Thus, social media has become a perfect place for users’ malicious behaviors like committing hate crimes, spreading toxic information, abetting crimes, etc. Malicious behaviors are covert and widespread, with potential relevance regarding topic, person, place, and so on. Therefore, it is necessary to develop novel techniques to detect and disrupt malicious behavior on social media effectively. Previous research has shown promising results in extracting semantic text (speech) representation using natural language processing methods. Yet the latent relation between speeches and the connection between users behind speeches is rarely explored. In light of this, we propose a holistic model named Graph adaption BERT (GraphBERT) to detect malicious behaviors on Twitter with both semantic and relational information. Specifically, we first present a novel and a large-scale corpus of tweet data to benefit both graph-based and language-based malicious behavior detection research. Then, we design a novel model GraphBERT to learn comprehensive tweet and user representation with the integration of both semantic information encoded by transformers (i.e., BERT) and relational information encoded by graph neural network. GraphBERT further leverages a weight adaption BERT module implemented between transformer layers to refine tweet embedding using relational information for malicious tweet classification. Finally, the adapted tweet embedding is used with the initial tweet representation to generate user embedding for malicious user detection. The extensive experiments on the collected Twitter data show that our model outperforms the state-of-the-art baseline methods for both tasks (i.e., malicious tweet classification and malicious user detection).

Representation Learning is a significant and challenging task in multimodal learning. Effective modality representations should contain two parts of characteristics: the consistency and the difference. Due to the unified multimodal annota- tion, existing methods are restricted in capturing differenti- ated information. However, additional unimodal annotations are high time- and labor-cost. In this paper, we design a la- bel generation module based on the self-supervised learning strategy to acquire independent unimodal supervisions. Then, joint training the multimodal and uni-modal tasks to learn the consistency and difference, respectively. Moreover, dur- ing the training stage, we design a weight-adjustment strat- egy to balance the learning progress among different sub- tasks. That is to guide the subtasks to focus on samples with the larger difference between modality supervisions. Last, we conduct extensive experiments on three public multimodal baseline datasets. The experimental results validate the re- liability and stability of auto-generated unimodal supervi- sions. On MOSI and MOSEI datasets, our method surpasses the current state-of-the-art methods. On the SIMS dataset, our method achieves comparable performance than human- annotated unimodal labels. The full codes are available at https://github.com/thuiar/Self-MM.

Many computer scientists use the aggregated answers of online workers to represent ground truth. Prior work has shown that aggregation methods such as majority voting are effective for measuring relatively objective features. For subjective features such as semantic connotation, online workers, known for optimizing their hourly earnings, tend to deteriorate in the quality of their responses as they work longer. In this paper, we aim to address this issue by proposing a quality-aware semantic data annotation system. We observe that with timely feedback on workers’ performance quantified by quality scores, better informed online workers can maintain the quality of their labeling throughout an extended period of time. We validate the effectiveness of the proposed annotation system through i) evaluating performance based on an expert-labeled dataset, and ii) demonstrating machine learning tasks that can lead to consistent learning behavior with 70%–80% accuracy. Our results suggest that with our system, researchers can collect high-quality answers of subjective semantic features at a large scale.

Previous studies in multimodal sentiment analysis have used limited datasets, which only contain unified multimodal annotations.However, the unified annotations do not always reflect the independent sentiment of single modalities and limit the model to capture the difference between modalities.In this paper, we introduce a Chinese single-and multimodal sentiment analysis dataset, CH-SIMS, which contains 2,281 refined video segments in the wild with both multimodal and independent unimodal annotations.It allows researchers to study the interaction between modalities or use independent unimodal annotations for unimodal sentiment analysis.Furthermore, we propose a multi-task learning framework based on late fusion as the baseline.Extensive experiments on the CH-SIMS show that our methods achieve state-of-the-art performance and learn more distinctive unimodal representations.