Model for Anisotropic Directed Percolation

We propose a simulation model to study the properties of directed percolation in two-dimensional (2D) anisotropic random media. The degree of anisotropy in the model is given by the ratio $\mu$ between the axes of a semi-ellipse enclosing the bonds that promote percolation in one direction. At percolation, this simple model shows that the average number of bonds per site in 2D is an invariant equal to 2.8 independently of $\mu$. This result suggests that Sinai's theorem proposed originally for isotropic percolation is also valid for anisotropic directed percolation problems. The new invariant also yields a constant fractal dimension $D_{f} \sim 1.71$ for all $\mu$, which is the same value found in isotropic directed percolation (i.e., $\mu = 1$).Comment: RevTeX, 9 pages, 3 figures. To appear in Phys.Rev.

Still No Lie Detector for Language Models: Probing Empirical and Conceptual Roadblocks

We consider the questions of whether or not large language models (LLMs) have beliefs, and, if they do, how we might measure them. First, we evaluate two existing approaches, one due to Azaria and Mitchell (2023) and the other to Burns et al. (2022). We provide empirical results that show that these methods fail to generalize in very basic ways. We then argue that, even if LLMs have beliefs, these methods are unlikely to be successful for conceptual reasons. Thus, there is still no lie-detector for LLMs. After describing our empirical results we take a step back and consider whether or not we should expect LLMs to have something like beliefs in the first place. We consider some recent arguments aiming to show that LLMs cannot have beliefs. We show that these arguments are misguided. We provide a more productive framing of questions surrounding the status of beliefs in LLMs, and highlight the empirical nature of the problem. We conclude by suggesting some concrete paths for future work

iSTART: Interactive Strategy Training for Active Reading and Thinking

Interactive Strategy Training for Active Reading and Thinking (iSTART) is a Web-based application that provides young adolescent to college-age students with high-level reading strategy training to improve comprehension of science texts. iSTART is modeled after an effective, human-delivered intervention called self-explanation reading training (SERT), which trains readers to use active reading strategies to self-explain difficult texts more effectively. To make the training more widely available, the Web-based trainer has been developed. Transforming the training from a human-delivered application to a computer-based one has resulted in a highly interactive trainer that adapts its methods to the performance of the students. The iSTART trainer introduces the strategies in a simulated classroom setting with interaction between three animated characters—an instructor character and two student characters— and the human trainee. Thereafter, the trainee identifies the strategies in the explanations of a student character who is guided by an instructor character. Finally, the trainee practices self-explanation under the guidance of an instructor character. We describe this system and discuss how appropriate feedback is generated

iSTART 2: Improvements for Efficiency and Effectiveness

iSTART (interactive strategy training for active reading and thinking) is a Web-based reading strategy trainer that develops students\u27 ability to self-explain difficult text as a means to improving reading comprehension. Its curriculum consists of modules presented interactively by pedagogical agents: an introduction to the basics of using reading strategies in the context of self-explanation, a demonstration of self-explanation, and a practice module in which the trainee generates self-explanations with feedback on the quality of reading strategies contained in the self-explanations. We discuss the objectives that guided the development of the second version of iSTART toward the goals of increased efficiency for the experimenters and effectiveness in the training. The more pedagogically challenging high school audience is accommodated by (1) a new introduction that increases interactivity, (2) a new demonstration with more and better focused scaffolding, and (3) a new practice module that provides improved feedback and includes a less intense but more extended regimen. Version 2 also benefits experimenters, who can set up and evaluate experiments with less time and effort, because pre- and post testing has been fully computerized and the process of preparing a text for the practice module has been reduced from more than 1 person-week to about an hour\u27s time