25 citations · 26 across the 5 of their papers we have counts for
5 papers
How LLMs Comprehend Temporal Meaning in Narratives: A Case Study in Cognitive Evaluation of LLMs
Karin de Langis, Jong Inn Park, Andreas Schramm +5
Large language models (LLMs) exhibit increasingly sophisticated linguistic capabilities, yet the extent to which these behaviors reflect human-like cognition versus advanced patter…
Stealing Creator's Workflow: A Creator-Inspired Agentic Framework with Iterative Feedback Loop for Improved Scientific Short-form Generation
Jong Inn Park, Maanas Taneja, Qianwen Wang +1
Generating engaging, accurate short-form videos from scientific papers is challenging due to content complexity and the gap between expert authors and readers. Existing end-to-end…
Strong Memory, Weak Control: An Empirical Study of Executive Functioning in LLMs
Karin de Langis, Jong Inn Park, Bin Hu +5
Working memory, or the ability to hold and manipulate information in the mind, is a critical component of human intelligence and executive functioning. It is correlated with perfor…
SelectLLM: Can LLMs Select Important Instructions to Annotate?
Ritik Sachin Parkar, Jaehyung Kim, Jong Inn Park +1
Instruction tuning benefits from large and diverse datasets; however, creating such datasets involves a high cost of human labeling. While synthetic datasets generated by large lan…
Benchmarking Cognitive Biases in Large Language Models as Evaluators
Ryan Koo, Minhwa Lee, Vipul Raheja +3
Large Language Models are cognitively biased judges. Large Language Models (LLMs) have recently been shown to be effective as automatic evaluators with simple prompting and in-cont…