ACCD detects coordinated online harm campaigns by combining adaptive causal temporal scoring, semi-supervised account classification, and stability validation. On a filtered Twitter IRA task, it improves account-level coordination screening over correlation and fixed-parameter CCM baselines, while reducing manual labeling and runtime.

Coordinated inauthentic behavior (CIB) threatens the integrity of public conversation, yet existing detectors remain unreliable in practice. Most systems measure correlation between account activities, which conflates genuine influence with shared reactions to trending topics. They also apply fixed detection parameters and require extensive manual labeling, so configurations tuned on one campaign rarely transfer to another. We present ACCD (Adaptive Causal Coordination Detection), a framework that treats this problem as memory-guided causal inference. ACCD screens account pairs in three stages that each narrow the candidate set: it retrieves convergent-cross-mapping parameters from a context-indexed memory of past performance to estimate causal influence, concentrates annotation on uncertain accounts through active learning in a semi-supervised classifier whose labels filter the surviving pairs, and validates the remainder with estimators and thresholds chosen from prior experience. On the Twitter Internet Research Agency (IRA) dataset, ACCD improves pair-level F1 from 75.8% to 87.3% over a fixed-parameter causal baseline, reduces manual labeling by 68.3%, and cuts end-to-end detection time by 2.5×. On Reddit coordination traces it reaches 84.7% F1. On synthetic data with known causal structure, it recovers true influence edges at 0.934 area under the ROC curve (AUC) against 0.708 for correlation-based detection, and the gap widens to 0.861 against 0.586 once strong confounding is introduced. These results suggest that adapting causal configurations to detection context provides a practical and efficient screening tool for protecting online public discourse.

Large language models (LLMs) have been increasingly explored for graph computation, where tasks require reasoning over structured relationships and algorithmic operations. Yet, it remains unclear when LLMs can reliably support such computation and how they should be incorporated into graph-solving pipelines. Existing surveys at the intersection of LLMs and graphs primarily focus on graph learning, text-attributed graphs, or graph-language modeling. To bridge this gap, we provide a comprehensive review of LLMs for graph computation through a role-based taxonomy. Specifically, we identify two major paradigms: i) LLMs as executors, where models directly solve graph tasks from graph descriptions and instructions; and ii) LLMs as planners, where models formulate problems, decompose reasoning steps, and invoke external tools or agents for execution. Based on this taxonomy, we analyze the strengths and limitations of current methods. Our review indicates that LLMs are promising for simple, small-scale tasks, but remain unreliable for large-scale and exactness-demanding tasks. Finally, we summarize available datasets and suggest four future directions.

We explore implicit opinion extraction as a new component of aspect-based sentiment analysis (ABSA) systems. Prior work in ABSA has investigated opinion extraction as an important subtask, however, these works only label concise, explicitly-stated opinion spans. In this work, we present Shoes-ACOSI, a new and challenging ABSA dataset in the e-commerce domain with implicit opinion span annotations, the first of its kind. Shoes-ACOSI builds upon the existing Aspect-Category-Opinion-Sentiment (ACOS) quadruple extraction task, extending the task to quintuple extraction—now localizing and differentiating both implicit and explicit opinion. In addition to the new annotation schema, our dataset contains paragraph-length inputs which, importantly, present complex challenges through increased input length, increased number of sentiment expressions, and more mixed-sentiment-polarity examples when compared with existing benchmarks. We quantify the difficulty of our new dataset by evaluating with state-of-the-art fully-supervised and prompted-LLM baselines. We find our dataset presents significant challenges for both supervised models and LLMs, particularly from the new implicit opinion extraction component of the ACOSI task, highlighting the need for continued research into implicit opinion understanding.

In a semantic frame resource such as FrameNet, the definition sentence of a frame is essential for humans to understand the meaning of the frame intuitively. Recently, several attempts have been made to induce semantic frames from large corpora, but the cost of creating the definition sentences for such frames is significant. In this paper, we address a new task of generating frame definitions from a set of frame-evoking words. Specifically, given a cluster of frame-evoking words and associated exemplars induced as the same semantic frame, we utilize a large language model to generate frame definitions. We demonstrate that incorporating frame element reasoning as chain-of-thought can enhance the inclusion of correct frame elements in the generated definitions.

“From pre-trained language model (PLM) to large language model (LLM), the field of naturallanguage processing (NLP) has witnessed steep performance gains and wide practical uses. Theevaluation of a research field guides its direction of improvement. However, LLMs are extremelyhard to thoroughly evaluate for two reasons. First of all, traditional NLP tasks become inade-quate due to the excellent performance of LLM. Secondly, existing evaluation tasks are difficultto keep up with the wide range of applications in real-world scenarios. To tackle these problems,existing works proposed various benchmarks to better evaluate LLMs. To clarify the numerousevaluation tasks in both academia and industry, we investigate multiple papers concerning LLMevaluations. We summarize 4 core competencies of LLM, including reasoning, knowledge, relia-bility, and safety. For every competency, we introduce its definition, corresponding benchmarks,and metrics. Under this competency architecture, similar tasks are combined to reflect corre-sponding ability, while new tasks can also be easily added into the system. Finally, we give oursuggestions on the future direction of LLM’s evaluation.”

Interlingual homographs are words that spell the same but possess different meanings across languages. Recognizing interlingual homographs from form-identical words generally needs linguistic knowledge and massive annotation work. In this paper, we propose an automatic interlingual homograph recognition method based on the cross-lingual word embedding similarity and co-occurrence of form-identical words in parallel sentences. We conduct experiments with various off-the-shelf language models coordinating with cross-lingual alignment operations and co-occurrence metrics on the Chinese-Japanese and English-Dutch language pairs. Experimental results demonstrate that our proposed method is able to make accurate and consistent predictions across languages.