As large language models (LLMs) are deployed globally, creating pluralistic systems that can accommodate the diverse preferences and values of users worldwide becomes essential. We introduce EVALUESTEER, a benchmark to measure LLMs' and reward models' (RMs) steerability towards users' value and stylistic preference profiles grounded in psychology and human-LLM interaction literature. To address the gap in existing datasets that do not support controlled evaluations of RM steering, we synthetically generated 165,888 preference pairs -- systematically varying pairs along 4 value dimensions (traditional, secular-rational, survival, and self-expression) and 4 style dimensions (verbosity, readability, confidence, and warmth). We use EVALUESTEER to evaluate whether, given a user profile and a pair of candidate value-laden and style-laden responses, LLMs and RMs are able to select the output that aligns with the user's preferences. We evaluate six open-source and proprietary LLMs and RMs under eleven systematic prompting conditions and six preference comparison scenarios. Notably, our results show that, when given the user's full profile of values and stylistic preferences, the best models achieve <75% accuracy at choosing the correct response, in contrast to >99% accuracy when only relevant style and value preferences are provided. EVALUESTEER thus highlights the limitations of current RMs at identifying and adapting to relevant user profile information, and provides a challenging testbed for developing RMs that can be steered towards diverse human values and preferences.

Things to Do in L.A. Tap to enable a layout that focuses on the article. Vanessa Silver, who tutors young children with dyslexia, works with Liina Yerro, 9, in Granada Hills. This is read by an automated voice. Please report any issues or inconsistencies here . California to begin universal screening of kindergarten through second-grade students for reading difficulties, including dyslexia.

Dyslexia, affecting an estimated 10% to 20% of the global population, significantly impairs learning capabilities, highlighting the need for innovative and accessible diagnostic methods. This paper investigates the effectiveness of eye-tracking technology combined with machine learning algorithms as a cost-effective alternative for early dyslexia detection. By analyzing general eye movement patterns, including prolonged fixation durations and erratic saccades, we proposed an enhanced solution for determining eye-tracking-based dyslexia features. A Random Forest Classifier was then employed to detect dyslexia, achieving an accuracy of 88.58\%. Additionally, hierarchical clustering methods were applied to identify varying severity levels of dyslexia. The analysis incorporates diverse methodologies across various populations and settings, demonstrating the potential of this technology to identify individuals with dyslexia, including those with borderline traits, through non-invasive means. Integrating eye-tracking with machine learning represents a significant advancement in the diagnostic process, offering a highly accurate and accessible method in clinical research.

The aim of the TextAD research project at Linköping University is to better understand different types of reading difficulties. This knowledge can be used to develop digital tools that automatically adapt texts to the needs of readers. For many people, reading can be difficult, and some people need texts adapted to their ability. This group is very diverse, and the aspects of reading that cause problems differ from one individual to another. The project will investigate how three groups aged 13-17 years (pupils with dyslexia, pupils with intellectual disabilities, and typical readers) understand texts that present factual information.

The system is the first of its kind worldwide and has the potential to help roughly 400,000 Danes with dyslexia and other reading difficulties. Personalized text simplification - that's the gist of a recently completed PhD project from the University of Copenhagen's Department of Computer Science. Postdoc Joachim Bingel's system makes it easier for dyslexics and others with reading difficulties to read texts online. The artificial intelligence-based software replaces difficult words in sentences with simpler alternatives, while learning which words, endings, etc. a user is having particular difficulty with. "We live in a knowledge-based society where anyone without access to knowledge and information due to reading or language difficulties is quickly sidelined," according to head researcher, Postdoc Joachim Bingel.

In the beginning of 2016 Jill Watson, an IBM-designed bot, has been helping graduate students at Georgia Institute of Technology solve problems with their design projects. Responding to questions over email and posted on forums, Jill had a casual, colloquial tone, and was able to offer nuanced and accurate responses within minutes. A robot has been teaching graduate students for 5 months and none of them realized. Here are just a few of artificial intelligence tools and technologies that will shape and define the educational experience of the future. Duolingo is the world's most popular platform to learn a language.