The quest for human imitative AI has been an enduring topic in AI research since inception. The technical evolution and emerging capabilities of the latest cohort of large language models (LLMs) have reinvigorated the subject beyond academia to cultural zeitgeist. While recent NLP evaluation benchmark tasks test some aspects of human-imitative behaviour (e.g., BIG-bench's `human-like behavior' tasks), few, if not none, examine creative problem solving abilities. Creative problem solving in humans is a well-studied topic in cognitive neuroscience with standardized tests that predominantly use ability to associate (heterogeneous) connections among clue words as a metric for creativity. Exposure to misleading stimuli --- distractors dubbed red herrings --- impede human performance in such tasks via the fixation effect and Einstellung paradigm. In cognitive neuroscience studies, such fixations are experimentally induced by pre-exposing participants to orthographically similar incorrect words to subsequent word-fragments or clues. The popular British quiz show Only Connect's Connecting Wall segment essentially mimics Mednick's Remote Associates Test (RAT) formulation with built-in, deliberate red herrings, that makes it an ideal proxy dataset to explore and study fixation effect and Einstellung paradigm from cognitive neuroscience in LLMs. In addition to presenting the novel Only Connect Wall (OCW) dataset, we also report results from our evaluation of selected pre-trained language models and LLMs (including OpenAI's GPT series) on creative problem solving tasks like grouping clue words by heterogeneous connections, and identifying correct open knowledge domain connections in respective groups. We synthetically generate two additional datasets: OCW-Randomized, OCW-WordNet to further analyze our red-herrings hypothesis in language models.The code and link to the dataset is available at url.

Twitter is one of the social media platforms that people express themselves freely. Harassment is one consequence of these such platforms, which is hard to obstruct. Text categorization and classification is a task that aims to solve this problem. Several studies applied classical machine learning methods and recent deep neural networks to categorize the text. However, only a few studies have explored graph convolutional neural networks while using classical approaches to categorize harassment Tweets. In this work, we propose using graph convolutional networks (GCN) for tweet categorization. Second, we explore this categorization task using classical machine learning approaches and compare the results with the GCN model. Third, we show the effectiveness of the GCN model on this problem by the other evaluation of the model on fewer sample datasets. In addition, we used different embedding approaches to find the best representation for the dataset in each of the models and represent the best embedding approach to use in this problem.