E-commerce live streaming in China, particularly on platforms like Douyin, has become a major sales channel, but hosts often use morphs to evade scrutiny and engage in false advertising. This study introduces the Live Auditory Morph Resolution (LiveAMR) task to detect such violations. Unlike previous morph research focused on text-based evasion in social media and underground industries, LiveAMR targets pronunciation-based evasion in health and medical live streams. We constructed the first LiveAMR dataset with 86,790 samples and developed a method to transform the task into a text-to-text generation problem. By leveraging large language models (LLMs) to generate additional training data, we improved performance and demonstrated that morph resolution significantly enhances live streaming regulation.

Research on text simplification has been ongoing for many years, yet document simplification remains a significant challenge due to the need to address complex factors such as technical terminology, metaphors, and overall coherence. In this work, we introduce a novel multi-agent framework AgentSimp for document simplification, based on large language models. This framework simulates the collaborative efforts of a team of human experts through the roles played by multiple agents, effectively meeting the intricate demands of document simplification. We investigate two communication strategies among agents (pipeline-style and synchronous) and two document reconstruction strategies (Direct and Iterative). According to both automatic evaluation metrics and human evaluation results, AgentSimp produces simplified documents that are more thoroughly simplified and more coherent across various articles and styles.

Research on text simplification has been ongoing for many years. However, the task of document simplification (DS) remains a significant challenge due to the need to consider complex factors such as technical terminology, metaphors, and overall coherence. In this work, we introduce a novel multi-agent framework for document simplification (AgentSimp) based on large language models (LLMs). This framework emulates the collaborative process of a human expert team through the roles played by multiple agents, addressing the intricate demands of document simplification. We explore two communication strategies among agents (pipeline-style and synchronous) and two document reconstruction strategies (Direct and Iterative ). According to both automatic evaluation metrics and human evaluation results, the documents simplified by AgentSimp are deemed to be more thoroughly simplified and more coherent on a variety of articles across different types and styles.

Existing lexical substitution (LS) benchmarks were collected by asking human annotators to think of substitutes from memory, resulting in benchmarks with limited coverage and relatively small scales. To overcome this problem, we propose a novel annotation method to construct an LS dataset based on human and machine collaboration. Based on our annotation method, we construct the first Chinese LS dataset CHNLS which consists of 33,695 instances and 144,708 substitutes, covering three text genres (News, Novel, and Wikipedia). Specifically, we first combine four unsupervised LS methods as an ensemble method to generate the candidate substitutes, and then let human annotators judge these candidates or add new ones. This collaborative process combines the diversity of machine-generated substitutes with the expertise of human annotators. Experimental results that the ensemble method outperforms other LS methods. To our best knowledge, this is the first study for the Chinese LS task.

“Relation Extraction (RE) task aims to discover the semantic relation that holds between two entitiesand contributes to many applications such as knowledge graph construction and completion. Reinforcement Learning (RL) has been widely used for RE task and achieved SOTA results, whichare mainly designed with rewards to choose the optimal actions during the training procedure,to improve RE’s performance, especially for low-resource conditions. Recent work has shownthat offline or online RL can be flexibly formulated as a sequence understanding problem andsolved via approaches similar to large-scale pre-training language modeling. To strengthen theability for understanding the semantic signals interactions among the given text sequence, thispaper leverages Transformer architecture for RL-based RE methods, and proposes a genericframework called Transformer Enhanced RL (TERL) towards RE task. Unlike prior RL-basedRE approaches that usually fit value functions or compute policy gradients, TERL only outputsthe best actions by utilizing a masked Transformer. Experimental results show that the proposedTERL framework can improve many state-of-the-art RL-based RE methods.”