Deanthropomorphising NLP: Can a Language Model Be Conscious?

This work is intended as a voice in the discussion over the recent claims that LaMDA, a pretrained language model based on the Transformer model architecture, is sentient. This claim, if confirmed, would have serious ramifications in the Natural Language Processing (NLP) community due to wide-spread use of similar models. However, here we take the position that such a language model cannot be sentient, or conscious, and that LaMDA in particular exhibits no advances over other similar models that would qualify it. We justify this by analysing the Transformer architecture through Integrated Information Theory. We see the claims of consciousness as part of a wider tendency to use anthropomorphic language in NLP reporting. Regardless of the veracity of the claims, we consider this an opportune moment to take stock of progress in language modelling and consider the ethical implications of the task. In order to make this work helpful for readers outside the NLP community, we also present the necessary background in language modelling

Volumetric properties of binary mixtures of 2,4,6-trimethylpyridine with 1,2-ethanediol, methanol, and water, and the association energies of the O-H...N bonded complexes

2,4,6-Trimethylpyridine forms 1:1 complexes with methanol, 1,2-ethanediol, and water due to the O–H· · ·N bonds. The association energy of the complexes was calculated usingMP2 and DFT methods. The complexes with 1,2-ethanediol and water aggregate in the liquid phase as a result of the O–H· · ·O bonds. In spite of the higher O–H· · ·N bond energy, the aggregation of the ethanediolic complexes is less pronounced than that of the aqueous ones. That is probably caused by the weaker induction effect due to the C–C chain separating the hydroxyl groups in the diol molecule. Aggregation is impossible in the methanolic system, because of the lack of proton-donating functional groups. Differences in the hydrogen bond energy and in the ability to aggregate are manifested in the volumetric properties of the mixtures

BODEGA: Benchmark for Adversarial Example Generation in Credibility Assessment

Text classification methods have been widely investigated as a way to detect content of low credibility: fake news, social media bots, propaganda, etc. Quite accurate models (likely based on deep neural networks) help in moderating public electronic platforms and often cause content creators to face rejection of their submissions or removal of already published texts. Having the incentive to evade further detection, content creators try to come up with a slightly modified version of the text (known as an attack with an adversarial example) that exploit the weaknesses of classifiers and result in a different output. Here we introduce BODEGA: a benchmark for testing both victim models and attack methods on four misinformation detection tasks in an evaluation framework designed to simulate real use-cases of content moderation. We also systematically test the robustness of popular text classifiers against available attacking techniques and discover that, indeed, in some cases barely significant changes in input text can mislead the models. We openly share the BODEGA code and data in hope of enhancing the comparability and replicability of further research in this area

Using NLP to quantify the environmental cost and diversity benefits of in-person NLP conferences

The environmental costs of research are progressively important to the NLP community and their associated challenges are increasingly debated. In this work, we analyse the carbon cost (measured as CO2-equivalent) associated with journeys made by researchers attending in-person NLP conferences. We obtain the necessary data by text-mining all publications from the ACL anthology available at the time of the study (n=60,572) and extracting information about an author's affiliation, including their address. This allows us to estimate the corresponding carbon cost and compare it to previously known values for training large models. Further, we look at the benefits of in-person conferences by demonstrating that they can increase participation diversity by encouraging attendance from the region surrounding the host country. We show how the trade-off between carbon cost and diversity of an event depends on its location and type. Our aim is to foster further discussion on the best way to address the joint issue of emissions and diversity in the future

Exit probability in a one-dimensional nonlinear q-voter model

We formulate and investigate the nonlinear $q$-voter model (which as special cases includes the linear voter and the Sznajd model) on a one dimensional lattice. We derive analytical formula for the exit probability and show that it agrees perfectly with Monte Carlo simulations. The puzzle, that we deal with here, may be contained in a simple question: "Why the mean field approach gives the exact formula for the exit probability in the one-dimensional nonlinear $q$-voter model?". To answer this question we test several hypothesis proposed recently for the Sznajd model, including the finite size effects, the influence of the range of interactions and the importance of the initial step of the evolution. On the one hand, our work is part of a trend of the current debate on the form of the exit probability in the one-dimensional Sznajd model but on the other hand, it concerns the much broader problem of nonlinear $q$-voter model

Clinical and classic echocardiographic features of patients with, and without, left ventricle reverse remodeling following the introduction of cardiac resynchronization therapy

Background: The aim of the study was to assess clinical and classic echocardiographic data in patients with different cardiac resynchronization therapy (CRT) outcomes. Methods: Sixty consecutive patients (aged 66.3 ± 8.7 years, 57 men) with chronic heart failure (CHF) in New York Heart Association (NYHA) classes III–IV despite optimized pharmacotherapy, with left ventricular end-diastolic diameter (LVEDD) > 55 mm, left ventricular ejection fraction £ 35% and wide QRS complex (≥ 120 ms), including individuals with permanent atrial fibrillation (AF) and single- and dual-chamber pacing, were assessed firstly before, and secondly three months after, biventricular heart stimulator implantation (excluding three patients who died during the follow-up). Patients developing ≥ 10% reduction of left ventricular end-systolic volume (LVESV) were classified as responders to CRT. Results: The group of responders (n = 34, 59.7%) and the group of non-responders (n = 23, 40.3%) did not differ regarding baseline echocardiographic parameters or in terms of clinical data of age, gender, concomitant diseases, smoking or pharmacological treatment. The differences involved higher rates of ischemic CHF background, prevalence of hypertension and permanent AF, and a higher concentration of N-terminal pro-B-type natriuretic peptide (NT-proBNP) among the non-responders. In the multivariate logistic regression analysis, NT-proBNP, body mass index (BMI) and the presence of permanent AF correlated negatively with the magnitude of LVESV reduction following CRT introduction. Conclusions: Classic echocardiographic data did not predict left ventricle reverse remodeling. Higher rates of ischemic CHF aetiology, hypertension, permanent AF and higher NT-proBNP concentration were found in the group without at least 10% LVESV reduction at the three month follow-up. NT-proBNP, BMI and the presence of permanent AF had negative effects on the magnitude of LVESV. (Cardiol J 2011; 18, 2: 157-164