Why think step-by-step? Reasoning emerges from the locality of experience

Humans have a powerful and mysterious capacity to reason. By working through a series of purely mental steps, we can make inferences we would not be capable of making directly -- despite that fact that we get no additional data from the world. Similarly, large language models can perform better at complex tasks through chain-of-thought reasoning, where they generate intermediate steps before answering a question. We use language models to investigate the questions of when and why reasoning is helpful, testing the hypothesis that reasoning is effective when training data consisting of local clusters of variables that influence each other strongly. These training conditions enable the chaining of accurate local inferences in order to estimate relationships between variables that were not seen together in training. We train an autoregressive transformer on samples from joint distributions defined by Bayes nets, but only include a subset of all the variables in each sample. We compare language models' ability to match conditional probabilities both with and without intermediate reasoning steps, finding that intermediate steps help only when the training data is locally structured with respect to dependencies between variables. Furthermore, intermediate variables need to be relevant to the relationship between observed information and target inferences. Our results illustrate how the statistical structure of training data drives the effectiveness of reasoning step by step.Comment: 8 pages, 3 figure

My Two Ears Can Witness : Feminist Pedagogy from Rehearsal Hall to Classroom

Given that university rehearsal halls are a natural home for feminist pedagogy, this paper addresses professors across campus under the contention that the signature pedagogy of theatre offers a model for faculty in other disciplines. The essay adapts a series of rehearsal hall techniques for traditional classrooms as efficient ways of fostering subjectivity, empowerment, community, and reflection in service of socio-cultural ends. The original teaching activities outlined herein do not require theatrical performance, but they nevertheless draw upon the power of live witnessing and interactive response that make theatre a powerful pedagogical tool. The authors conclude with an illustration of how their techniques impacted a unique performance of Shakespeare\u27s Comedy of Errors and call for greater interaction between liberatory pedagogues across the disciplines

Can co-authorship networks be used to predict author research impact? A machine-learning based analysis within the field of degenerative cervical myelopathy research.

INTRODUCTION: Degenerative Cervical Myelopathy (DCM) is a common and disabling condition, with a relatively modest research capacity. In order to accelerate knowledge discovery, the AO Spine RECODE-DCM project has recently established the top priorities for DCM research. Uptake of these priorities within the research community will require their effective dissemination, which can be supported by identifying key opinion leaders (KOLs). In this paper, we aim to identify KOLs using artificial intelligence. We produce and explore a DCM co-authorship network, to characterise researchers' impact within the research field. METHODS: Through a bibliometric analysis of 1674 scientific papers in the DCM field, a co-authorship network was created. For each author, statistics about their connections to the co-authorship network (and so the nature of their collaboration) were generated. Using these connectedness statistics, a neural network was used to predict H-Index for each author (as a proxy for research impact). The neural network was retrospectively validated on an unseen author set. RESULTS: DCM research is regionally clustered, with strong collaboration across some international borders (e.g., North America) but not others (e.g., Western Europe). In retrospective validation, the neural network achieves a correlation coefficient of 0.86 (p<0.0001) between the true and predicted H-Index of each author. Thus, author impact can be accurately predicted using only the nature of an author's collaborations. DISCUSSION: Analysis of the neural network shows that the nature of collaboration strongly impacts an author's research visibility, and therefore suitability as a KOL. This also suggests greater collaboration within the DCM field could help to improve both individual research visibility and global synergy

Psychologically-informed chain-of-thought prompts for metaphor understanding in large language models

Probabilistic models of language understanding are interpretable and structured, for instance models of metaphor understanding describe inference about latent topics and features. However, these models are manually designed for a specific task. Large language models (LLMs) can perform many tasks through in-context learning, but they lack the clear structure of probabilistic models. In this paper, we use chain-of-thought prompts to introduce structures from probabilistic models into LLMs. These prompts lead the model to infer latent variables and reason about their relationships to choose appropriate paraphrases for metaphors. The latent variables and relationships chosen are informed by theories of metaphor understanding from cognitive psychology. We apply these prompts to the two largest versions of GPT-3 and show that they can improve paraphrase selection.Comment: 12 pages, 1 figur

Logarithmically larger deletion codes of all distances

The deletion distance between two binary words $u,v \in \{0,1\}^n$ is the smallest $k$ such that $u$ and $v$ share a common subsequence of length $n-k$. A set $C$ of binary words of length $n$ is called a $k$-deletion code if every pair of distinct words in $C$ has deletion distance greater than $k$. In 1965, Levenshtein initiated the study of deletion codes by showing that, for $k\ge 1$ fixed and $n$ going to infinity, a $k$-deletion code $C\subseteq \{0,1\}^n$ of maximum size satisfies $\Omega_k(2^n/n^{2k}) \leq |C| \leq O_k( 2^n/n^k)$. We make the first asymptotic improvement to these bounds by showing that there exist $k$-deletion codes with size at least $\Omega_k(2^n \log n/n^{2k})$. Our proof is inspired by Jiang and Vardy's improvement to the classical Gilbert--Varshamov bounds. We also establish several related results on the number of longest common subsequences and shortest common supersequences of a pair of words with given length and deletion distance

Chitosan-Hyaluronate Hybrid Gel Intraarticular Injection Delays Osteoarthritis Progression and Reduces Pain in a Rat Meniscectomy Model as Compared to Saline and Hyaluronate Treatment

Chitosan-Hyaluronate hybrid gel (CHHG) is a self-forming thermo-responsive hydrogel. The current study was undertaken in order to assess the effect of CHHG on rat's surgically induced osteoarthritis. Methods. Thirteen rats were included in the study. In all rats weight-bearing was assessed using a Linton Incapacitance tester. All rats underwent bilateral medial partial meniscectomy. Four rats received a saline injection in the control knee and a 200-microliter injection of CHHG in the experimental knee. Five rats received a high-molecular weight hyaluronate injection to the control knee and a 200-microliter injection of CHHG in the experimental knee. Four rats underwent the same surgical procedure, allowed to recuperate for seven days and then CHHG and hyaluronate were injected. The animals were followed for 6 weeks. Two weeks after injection of a therapeutic substance the amount of weight-bearing on each knee was evaluated using a Linton Incapacitance meter. Results. Two weeks after induction of osteoarthritis there is less pain in the CHHG-treated knee than in the control-treated knee, as determined using a Lintron Incapacitance meter. After six-weeks the histological appearance of the CHHG-treated knee was superior to that of the controls. This is indicated by thicker cartilage remaining on the medial femoral condyle as well as less cyst formation in the CHHG-treated knee. Discussion. CHHG appears to delay progression of osteoarthritis and lessen pain in a rat surgically-induced knee osteoarthritis model. These results support other published results, indicating that there is an ameliorative effect of chitosan on human and rabbit osteoarthritis