The field of natural language processing is moving towards developing more efficient and safe methods for lexical simplification and text generation, with a focus on accessibility for individuals with cognitive impairments. Researchers are exploring the use of small language models and multi-task learning approaches to improve the accuracy and reliability of these systems. The development of new datasets and evaluation frameworks is also facilitating progress in this area. Notably, the use of discretized statistics and in-context learning is showing promise in reducing the complexity and improving the performance of these models. Noteworthy papers include:

• Towards Trustworthy Lexical Simplification, which proposes a framework for safe and efficient lexical simplification using small language models.
• DiSC-AMC, which presents a token- and parameter-efficient variant of in-context automatic modulation classification.
• Facilitating Cognitive Accessibility with LLMs, which investigates the potential of large language models to automate the generation of easy-to-read text.