Large language models are fixated by red herrings: exploring creative problem solving and einstellung effect using the only connect wall dataset

Conference item

The quest for human imitative AI has been an enduring topic in AI research since its inception. The technical evolution and emerging capabilities of the latest cohort of large language models (LLMs) have reinvigorated the subject beyond academia to the cultural zeitgeist. While recent NLP evaluation benchmark tasks test some aspects of human-imitative behavior (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 the 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, which makes it an ideal proxy task to explore and study the fixation effect and Einstellung paradigm from cognitive neuroscience in LLMs. In this paper, we present the novel Only Connect Wall (OCW) dataset and report results from our evaluation of selected pre-trained language models and LLMs 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 are available at https://github.com/TaatiTeam/OCW.

Authors: Naeini, SA; Saqur, R; Saeidi, M; Giorgi, J; Taati, B

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Curran Associates
• Host title: Advances in Neural Information Processing Systems 36 (NeurIPS 2023)
• Volume: 36
• Pages: 5631-5652
• Publication date: 2023-12-15
• Acceptance date: 2023-09-21
• Event title: 37th Conference on Neural Information Processing Systems (NeurIPS 2023)
• Event location: New Orleans, USA
• Event website: https://neurips.cc/Conferences/2023
• Event start date: 2023-12-10
• Event end date: 2023-12-16
• DOI: 10.52202/075280-0246
• EISBN: 9781713899921
• Language: English