SetLexSem Challenge: Using Set Operations to Evaluate the Lexical and Semantic Robustness of Language Models

Set theory is foundational to mathematics and, when sets are finite, to reasoning about the world. An intelligent system should perform set operations consistently, regardless of superficial variations in the operands. Initially designed for semantically-oriented NLP tasks, large language models (LLMs) are now being evaluated on algorithmic tasks. Because sets are comprised of arbitrary symbols (e.g. numbers, words), they provide an opportunity to test, systematically, the invariance of LLMs' algorithmic abilities under simple lexical or semantic variations. To this end, we present the SetLexSem Challenge, a synthetic benchmark that evaluates the performance of LLMs on set operations. SetLexSem assesses the robustness of LLMs' instruction-following abilities under various conditions, focusing on the set operations and the nature and construction of the set members. Evaluating seven LLMs with SetLexSem, we find that they exhibit poor robustness to variation in both operation and operands. We show -- via the framework's systematic sampling of set members along lexical and semantic dimensions -- that LLMs are not only not robust to variation along these dimensions but demonstrate unique failure modes in particular, easy-to-create semantic groupings of "deceptive" sets. We find that rigorously measuring language model robustness to variation in frequency and length is challenging and present an analysis that measures them independently. The code for reproducing the results of this paper, and for generating the SetLexSem Challenge dataset, is available at \href{https://github.com/amazon-science/SetLexSem-Challenge}{https://github.com/amazon-science/SetLexSem-Challenge}.

Clinical Applications of Plantar Pressure Measurement

Plantar pressure measurements can provide valuable insight into various health characteristics in patients. In this study, we describe different plantar pressure devices available on the market and their clinical relevance. Current devices are either platform-based or wearable and consist of a variety of sensor technologies: resistive, capacitive, piezoelectric, and optical. The measurements collected from any of these sensors can be utilized for a range of clinical applications including patients with diabetes, trauma, deformity and cerebral palsy, stroke, cervical myelopathy, ankle instability, sports injuries, and Parkinsons disease. However, the proper technology should be selected based on the clinical need and the type of tests being performed on the device. In this review we provide the reader with a simple overview of the existing technologies their advantages and disadvantages and provide application examples for each. Moreover, we suggest new areas in orthopaedic that plantar pressure mapping technology can be utilized for increased quality of care.