Language models show human-like content effects on reasoning

Abstract reasoning is a key ability for an intelligent system. Large language models achieve above-chance performance on abstract reasoning tasks, but exhibit many imperfections. However, human abstract reasoning is also imperfect, and depends on our knowledge and beliefs about the content of the reasoning problem. For example, humans reason much more reliably about logical rules that are grounded in everyday situations than arbitrary rules about abstract attributes. The training experiences of language models similarly endow them with prior expectations that reflect human knowledge and beliefs. We therefore hypothesized that language models would show human-like content effects on abstract reasoning problems. We explored this hypothesis across three logical reasoning tasks: natural language inference, judging the logical validity of syllogisms, and the Wason selection task (Wason, 1968). We find that state of the art large language models (with 7 or 70 billion parameters; Hoffman et al., 2022) reflect many of the same patterns observed in humans across these tasks -- like humans, models reason more effectively about believable situations than unrealistic or abstract ones. Our findings have implications for understanding both these cognitive effects, and the factors that contribute to language model performance

Label-free characterization of biochemical changes within human cells under parasite attack using synchrotron based micro-FTIR

© 2019 The Royal Society of Chemistry. The protozoan Toxoplasma gondii is responsible for severe, potentially life-threatening, infection in immunocompromised individuals and when acquired during pregnancy. In the meantime, there is no available vaccine and the anti-T. gondii drug arsenal is limited. An important challenge to improve antiparasitic therapy is to understand chemical changes that occur during infection. Here, we used Fourier transform infrared spectroscopy (FTIR) to investigate the effect of T. gondii infection on the chemical composition of human brain microvascular endothelial cells (hBMECs) at 3, 6, 24 and 48 hours postinfection (hpi). Principal component analysis (PCA) showed that the best separation and largest difference between infected and uninfected hBMECs was detected at 24 hpi and within the 3400-2800 cm-1 region. At 48 hpi, although the difference between samples was obvious within the 3400-2800 cm-1 region, more differences were detected in the fingerprint region. These findings indicate that infected and control cells can be easily distinguished. Although differences between the spectra varied, the separation was most clear at 24 hpi. T. gondii increased signals for lipids (2853 cm-1) and nucleic acids (976 cm-1, 1097 cm-1 and 1245 cm-1), and decreased signals for proteins (3289 cm-1, 2963 cm-1, 2875 cm-1) in infected cells compared to controls. These results, supported by amino acid levels in culture media, and global metabolomic and gene expression analyses of hBMECs, suggest that T. gondii parasite exploits a wide range of host-derived chemical compounds and signaling pathways for its own survival and proliferation within host cells. Our data demonstrate that FTIR combined with chemometric analysis is a valuable approach to elucidate the temporal, infection-specific, chemical alterations in host cells at a single cell resolution

Can language models learn from explanations in context?

Large language models can perform new tasks by adapting to a few in-context examples. For humans, rapid learning from examples can benefit from explanations that connect examples to task principles. We therefore investigate whether explanations of few-shot examples can allow language models to adapt more effectively. We annotate a set of 40 challenging tasks from BIG-Bench with explanations of answers to a small subset of questions, as well as a variety of matched control explanations. We evaluate the effects of various zero-shot and few-shot prompts that include different types of explanations, instructions, and controls on the performance of a range of large language models. We analyze these results using statistical multilevel modeling techniques that account for the nested dependencies among conditions, tasks, prompts, and models. We find that explanations of examples can improve performance. Adding untuned explanations to a few-shot prompt offers a modest improvement in performance; about 1/3 the effect size of adding few-shot examples, but twice the effect size of task instructions. We then show that explanations tuned for performance on a small validation set offer substantially larger benefits; building a prompt by selecting examples and explanations together substantially improves performance over selecting examples alone. Hand-tuning explanations can substantially improve performance on challenging tasks. Furthermore, even untuned explanations outperform carefully matched controls, suggesting that the benefits are due to the link between an example and its explanation, rather than lower-level features of the language used. However, only large models can benefit from explanations. In summary, explanations can support the in-context learning abilities of large language models o

Change management: The case of the elite sport performance team

The effective and efficient implementation of change is often required for both successful performance and management survival across a host of contemporary domains. However, although of major theoretical and practical significance, research to date has overlooked the application of change management (hereafter CM) knowledge to the elite sport performance team environment. Considering that the success of ‘off-field’ sports businesses are largely dependent on the performances of their ‘on-field’ team, this article explores the application of current CM theorizing to this specific setting and the challenges facing its utility. Accordingly, we identify the need and importance of developing theory specific to this area, with practical application in both sport and business, through examination of current knowledge and identification of the domain's unique, dynamic and contested properties. Markers of successful change are then suggested to guide initial enquiry before the article concludes with proposed lines of research which may act to provide a valid and comprehensive theoretical account of CM to optimize the research and practice of those working in the field

Mechanisms behind surface modification of polypropylene film using an atmospheric-pressure plasma jet

Plasma treatments are common for increasing the surface energy of plastics, such as polypropylene (PP), to create improved adhesive properties. Despite the significant differences in plasma sources and plasma properties used, similar effects on the plastic film can be achieved, suggesting a common dominant plasma constituent and underpinning mechanism. However, many details of this process are still unknown. Here we present a study into the mechanisms underpinning surface energy increase of PP using atmospheric-pressure plasmas. For this we use the effluent of an atmospheric-pressure plasma jet (APPJ) since, unlike most plasma sources used for these treatments, there is no direct contact between the plasma and the PP surface; the APPJ provides a neutral, radical-rich environment without charged particles and electric fields impinging on the PP surface. The APPJ is a RF-driven plasma operating in helium gas with small admixtures of O2 (0-1%), where the effluent propagates through open air towards the PP surface. Despite the lack of charged particles and electric fields on the PP surface, measurements of contact angle show a decrease from 93.9° to 70.1° in 1.4 s and to 35° in 120 s, corresponding to a rapid increase in surface energy from 36.4 mN m-1 to 66.5 mN m-1 in the short time of 1.4 s. These treatment effects are very similar to what is found in other devices, highlighting the importance of neutral radicals produced by the plasma. Furthermore, we find an optimum percentage of oxygen of 0.5% within the helium input gas, and a decrease of the treatment effect with distance between the APPJ and the PP surface. These observed effects are linked to two-photon absorption laser-induced fluorescence spectroscopy (TALIF) measurements of atomic oxygen density within the APPJ effluent which show similar trends, implying the importance of this radical in the surface treatment of PP. Analysis of the surface reveals a two stage mechanism for the production of polar bonds on the surface of the polymer: a fast reaction producing carboxylic acid, or a similar ketone, followed by a slower reaction that includes nitrogen from the atmosphere on the surface, producing amides from the ketones

Interaction of a TeV Scale Black Hole with the Quark-Gluon Plasma at LHC

If the fundamental Planck scale is near a TeV, then parton collisions with high enough center-of-mass energy should produce black holes. The production rate for such black holes has been extensively studied for the case of a proton-proton collision at \sqrt s = 14 TeV and for a lead-lead collision at \sqrt s = 5.5 TeV at LHC. As the parton energy density is much higher at lead-lead collisions than in pp collisions at LHC, one natural question is whether the produced black holes will be able to absorb the partons formed in the lead-lead collisions and eventually `eat' the quark-gluon plasma formed at LHC. In this paper, we make a quantitative analysis of this possibility and find that since the energy density of partons formed in lead-lead collisions at LHC is about 500 GeV/fm^3, the rate of absorption for one of these black holes is much smaller than the rate of evaporation. Hence, we argue that black holes formed in such collisions will decay very quickly, and will not absorb very many nearby partons. More precisely, we show that for the black hole mass to increase via parton absorption at the LHC the typical energy density of quarks and gluons should be of the order of 10^{10} GeV/fm^3. As LHC will not be able to produce such a high energy density partonic system, the black hole will not be able to absorb a sufficient number of nearby partons before it decays. The typical life time of the black hole formed at LHC is found to be a small fraction of a fm/c.Comment: 7 pages latex (double column), 3 eps figure

Tell me why! Explanations support learning relational and causal structure

Inferring the abstract relational and causal structure of the world is a major challenge for reinforcement-learning (RL) agents. For humans, language--particularly in the form of explanations--plays a considerable role in overcoming this challenge. Here, we show that language can play a similar role for deep RL agents in complex environments. While agents typically struggle to acquire relational and causal knowledge, augmenting their experience by training them to predict language descriptions and explanations can overcome these limitations. We show that language can help agents learn challenging relational tasks, and examine which aspects of language contribute to its benefits. We then show that explanations can help agents to infer not only relational but also causal structure. Language can shape the way that agents to generalize out-of-distribution from ambiguous, causally-confounded training, and explanations even allow agents to learn to perform experimental interventions to identify causal relationships. Our results suggest that language description and explanation may be powerful tools for improving agent learning and generalization.Comment: ICML 2022; 23 page

The Fall of Stringy de Sitter

Kachru, Kallosh, Linde, & Trivedi recently constructed a four-dimensional de Sitter compactification of IIB string theory, which they showed to be metastable in agreement with general arguments about de Sitter spacetimes in quantum gravity. In this paper, we describe how discrete flux choices lead to a closely-spaced set of vacua and explore various decay channels. We find that in many situations NS5-brane meditated decays which exchange NSNS 3-form flux for D3-branes are comparatively very fast.Comment: 35 pp (11 pp appendices), 5 figures, v3. fixed minor typo

Tailoring gold nanoparticle characteristics and the impact on aqueous-phase oxidation of glycerol

Poly(vinyl alcohol) (PVA)-stabilized Au nanoparticles (NPs) were synthesized by colloidal methods in which temperature variations (−75 to 75 °C) and mixed H2O/EtOH solvent ratios (0, 50, and 100 vol/vol) were used. The resulting Au NPs were immobilized on TiO2 (P25), and their catalytic performance was investigated for the liquid phase oxidation of glycerol. For each unique solvent system, there was a systematic increase in the average Au particle diameter as the temperature of the colloidal preparation increased. Generation of the Au NPs in H2O at 1 °C resulted in a high observed activity compared with current Au/TiO2 catalysts (turnover frequency = 915 h−1). Interestingly, Au catalysts with similar average particle sizes but prepared under different conditions had contrasting catalytic performance. For the most active catalyst, aberration-corrected high angle annular dark field scanning transmission electron microscopy analysis identified the presence of isolated Au clusters (from 1 to 5 atoms) for the first time using a modified colloidal method, which was supported by experimental and computational CO adsorption studies. It is proposed that the variations in the populations of these species, in combination with other solvent/PVA effects, is responsible for the contrasting catalytic properties