Think Twice: Perspective-Taking Improves Large Language Models' Theory-of-Mind Capabilities

Human interactions are deeply rooted in the interplay of thoughts, beliefs, and desires made possible by Theory of Mind (ToM): our cognitive ability to understand the mental states of ourselves and others. Although ToM may come naturally to us, emulating it presents a challenge to even the most advanced Large Language Models (LLMs). Recent improvements to LLMs' reasoning capabilities from simple yet effective prompting techniques such as Chain-of-Thought have seen limited applicability to ToM. In this paper, we turn to the prominent cognitive science theory "Simulation Theory" to bridge this gap. We introduce SimToM, a novel two-stage prompting framework inspired by Simulation Theory's notion of perspective-taking. To implement this idea on current ToM benchmarks, SimToM first filters context based on what the character in question knows before answering a question about their mental state. Our approach, which requires no additional training and minimal prompt-tuning, shows substantial improvement over existing methods, and our analysis reveals the importance of perspective-taking to Theory-of-Mind capabilities. Our findings suggest perspective-taking as a promising direction for future research into improving LLMs' ToM capabilities

Face-to-Face Contrastive Learning for Social Intelligence Question-Answering

Creating artificial social intelligence - algorithms that can understand the nuances of multi-person interactions - is an exciting and emerging challenge in processing facial expressions and gestures from multimodal videos. Recent multimodal methods have set the state of the art on many tasks, but have difficulty modeling the complex face-to-face conversational dynamics across speaking turns in social interaction, particularly in a self-supervised setup. In this paper, we propose Face-to-Face Contrastive Learning (F2F-CL), a graph neural network designed to model social interactions using factorization nodes to contextualize the multimodal face-to-face interaction along the boundaries of the speaking turn. With the F2F-CL model, we propose to perform contrastive learning between the factorization nodes of different speaking turns within the same video. We experimentally evaluated the challenging Social-IQ dataset and show state-of-the-art results

Difference-Masking: Choosing What to Mask in Continued Pretraining

The self-supervised objective of masking-and-predicting has led to promising performance gains on a variety of downstream tasks. However, while most approaches randomly mask tokens, there is strong intuition that deciding what to mask can substantially improve learning outcomes. We investigate this in continued pretraining setting in which pretrained models continue to pretrain on domain-specific data before performing some downstream task. We introduce Difference-Masking, a masking strategy that automatically chooses what to mask during continued pretraining by considering what makes a task domain different from the pretraining domain. Empirically, we find that Difference-Masking outperforms baselines on continued pretraining settings across four diverse language-only and multimodal video tasks

Estrogen transactivates EGFR via the sphingosine 1-phosphate receptor Edg-3: the role of sphingosine kinase-1

The transactivation of enhanced growth factor receptor (EGFR) by G protein–coupled receptor (GPCR) ligands is recognized as an important signaling mechanism in the regulation of complex biological processes, such as cancer development. Estrogen (E2), which is a steroid hormone that is intimately implicated in breast cancer, has also been suggested to function via EGFR transactivation. In this study, we demonstrate that E2-induced EGFR transactivation in human breast cancer cells is driven via a novel signaling system controlled by the lipid kinase sphingosine kinase-1 (SphK1). We show that E2 stimulates SphK1 activation and the release of sphingosine 1-phosphate (S1P), by which E2 is capable of activating the S1P receptor Edg-3, resulting in the EGFR transactivation in a matrix metalloprotease–dependent manner. Thus, these findings reveal a key role for SphK1 in the coupling of the signals between three membrane-spanning events induced by E2, S1P, and EGF. They also suggest a new signal transduction model across three individual ligand-receptor systems, i.e., “criss-cross” transactivation

Generating Images Instead of Retrieving Them : Relevance Feedback on Generative Adversarial Networks

Finding images matching a user’s intention has been largely basedon matching a representation of the user’s information needs withan existing collection of images. For example, using an exampleimage or a written query to express the information need and re-trieving images that share similarities with the query or exampleimage. However, such an approach is limited to retrieving onlyimages that already exist in the underlying collection. Here, wepresent a methodology for generating images matching the userintention instead of retrieving them. The methodology utilizes arelevance feedback loop between a user and generative adversarialneural networks (GANs). GANs can generate novel photorealisticimages which are initially not present in the underlying collection,but generated in response to user feedback. We report experiments(N=29) where participants generate images using four differentdomains and various search goals with textual and image targets.The results show that the generated images match the tasks andoutperform images selected as baselines from a fixed image col-lection. Our results demonstrate that generating new informationcan be more useful for users than retrieving it from a collection ofexisting information.Peer reviewe

Developing an Extracellular Vesicle Based Treatment for Osteoarthritis

Osteoarthritis (OA) is a disease characterized by the degradation of articular cartilage. Extracellular vesicles (EVs) are cargo-filled bodies that mediate intercellular communication and are influential in OA pathogenesis. This study utilized parallel methodologies to investigate whether EV signaling can be manipulated to combat OA. The first approach aimed to identify cells lines that produce EVs with therapeutic activity against OA, while the second introduced miRNA in EVs to induce cartilage regeneration. EVs derived from synovial fibroblasts (SFBs) induced further inflammation. Moreover, miRNA did not impact MMP-13 production. While SFB-EVs were pro-inflammatory, increasing the amount of MMP-13 present, human bone marrow-derived mesenchymal stem cell (BM-hMSC) EVs did not stimulate a change in MMP-13 production. Future studies should further characterize these results to maximize therapeutic impact

Li2NiO2F a new oxyfluoride disordered rocksalt cathode material

Lithium-rich disordered rocksalts such as Li1.3Nb0.3Mn0.4O2 and Li2MnO2F are being investigated as high energy density cathodes for next generation Li-ion batteries. They can support the (de) lithiation of lithium ions over large compositional ranges while preserving the same overall structure. Here, we present a new Ni-rich oxyfluoride cathode, Li2NiO2F, with a disordered rocksalt structure. Li2NiO2F and can deliver a discharge capacity of 200 mAh g−1 at an average voltage of 3.2 V