EEVR (Emotion Elicitation in Virtual Reality) is a novel dataset specifically designed for language supervision-based pre-training of emotion recognition tasks, such as valence and arousal classification. It features high-quality physiological signals, including electrodermal activity (EDA) and photoplethysmography (PPG), acquired through emotion elicitation via 360-degree virtual reality (VR) videos.Additionally, it includes subject-wise textual descriptions of emotions experienced during each stimulus gathered from qualitative interviews. The dataset consists of recordings from 37 participants and is the first dataset to pair raw text with physiological signals, providing additional contextual information that objective labels cannot offer. To leverage this dataset, we introduced the Contrastive Language Signal Pre-training (CLSP) method, which jointly learns representations using pairs of physiological signals and textual descriptions. Our results show that integrating self-reported textual descriptions with physiological signals significantly improves performance on emotion recognition tasks, such as arousal and valence classification. Moreover, our pre-trained CLSP model demonstrates strong zero-shot transferability to existing datasets, outperforming supervised baseline models, suggesting that the representations learned by our method are more contextualized and generalized. The dataset also includes baseline models for arousal, valence, and emotion classification, as well as code for data cleaning and feature extraction.

Gooey birch tar helped our distant cousins make weapons and possibly treat wounds. The bark of birch trees has been used to produce tar for more than 150,000 years. The center photo shows birch bark tar condensed onto a rock that borders a hearth. When scraped off the rocks, the viscous tar can be used as both an adhesive and antibiotic. Breakthroughs, discoveries, and DIY tips sent six days a week.

Large Language Model (LLM) agents have been increasingly adopted as simulation tools to model humans in social science and role-playing applications. However, one fundamental question remains: can LLM agents really simulate human behavior? In this paper, we focus on one critical and elemental behavior in human interactions, trust, and investigate whether LLM agents can simulate human trust behavior. We first find that LLM agents generally exhibit trust behavior, referred to as agent trust, under the framework of Trust Games, which are widely recognized in behavioral economics. Then, we discover that GPT-4 agents manifest high behavioral alignment with humans in terms of trust behavior, indicating the feasibility of simulating human trust behavior with LLM agents. In addition, we probe the biases of agent trust and differences in agent trust towards other LLM agents and humans. We also explore the intrinsic properties of agent trust under conditions including external manipulations and advanced reasoning strategies. Our study provides new insights into the behaviors of LLM agents and the fundamental analogy between LLMs and humans beyond value alignment. We further illustrate broader implications of our discoveries for applications where trust is paramount.

The proliferation of open-source Large Language Models (LLMs) from various institutions has highlighted the urgent need for comprehensive evaluation methods. However, current evaluation platforms, such as the widely recognized HuggingFace open LLM leaderboard, neglect a crucial aspect -- uncertainty, which is vital for thoroughly assessing LLMs. To bridge this gap, we introduce a new benchmarking approach for LLMs that integrates uncertainty quantification. Our examination involves nine LLMs (LLM series) spanning five representative natural language processing tasks. Our findings reveal that: I) LLMs with higher accuracy may exhibit lower certainty; II) Larger-scale LLMs may display greater uncertainty compared to their smaller counterparts; and III) Instruction-finetuning tends to increase the uncertainty of LLMs. These results underscore the significance of incorporating uncertainty in the evaluation of LLMs.

Interpreting hierarchical structures latent in language is a key limitation of current language models (LMs). While previous research has implicitly leveraged these hierarchies to enhance LMs, approaches for their explicit encoding are yet to be explored. To address this, we introduce a novel approach to re-train transformer encoder-based LMs as Hierarchy Transformer encoders (HiTs), harnessing the expansive nature of hyperbolic space. Our method situates the output embedding space of pre-trained LMs within a Poincaré ball with a curvature that adapts to the embedding dimension, followed by re-training on hyperbolic clustering and centripetal losses. These losses are designed to effectively cluster related entities (input as texts) and organise them hierarchically. We evaluate HiTs against pre-trained LMs, standard fine-tuned LMs, and several hyperbolic embedding baselines, focusing on their capabilities in simulating transitive inference, predicting subsumptions, and transferring knowledge across hierarchies. The results demonstrate that HiTs consistently outperform all baselines in these tasks, underscoring the effectiveness and transferability of our re-trained hierarchy encoders.

Female pastor is suspended after her shocking Epstein link is exposed... as she compares herself to JESUS while defending their relationship'Tell me to my face': Republican senator torches Noem's replacement as their vicious personal feud spills into public Outrageous full story of scandalous affair that's the talk of Manhattan's exclusive private schools: Family insiders reveal humiliating sex secrets... shock'confession' letter... and the furious relative who exposed it all Ugly new Nicole Kidman and Keith Urban divorce fight ERUPTS: Her friends share humiliating details of'midlife crisis'... and reveal brutal REAL reason daughter Sunday Rose'snubbed' him Perfect All-American family lived in stunning $1.1m Colorado mansion and bankrolled glamorous daughter's horse stables... now matriarch has sullied their good name with a HUGE scandal Meghan unveils new As Ever line with Lilibet... amid claims Netflix has been left with huge $10m surplus of her unsold products after'split' with streamer Woke Democrat, 26, who can't get out of bed in time for meetings loses primary to professor accused of inappropriate relationship by former student I watched the children's book author who poisoned her husband from 5ft away. This is the off-camera moment her mask finally slipped... it was truly chilling I ran America's only Supermax jail: What history's most notorious terrorists and serial killers told me as they waited to die Sinister truth about explosive resignation of Trump's top counter-terror chief Joe Kent... and his shock claim Israel is manipulating the president: MARK HALPERIN Hairdresser who weighs 300lbs says Southwest airport check-in worker looked him up and down and told him he'd have to buy extra seat Kim Kardashian takes a VERY dramatic tumble in towering $80 'stripper heels' and accidentally grabs an'old lady' as she falls on her way out of Vanity Fair Oscar party Everything JFK Jr told friends about his love affair with'sexual dynamo' Madonna... her unprintable pillow talk... and his perverse incest request that she couldn't go through with Saudi, UAE and Qatar energy facilities are evacuated after Iran threatens'full scale economic war' as oil price jumps 5%: Live updates New PILL for psoriasis approved... giving hope to millions suffering from debilitating skin condition How I lost 8st in my 50s and now finally have the figure of my dreams. I've been large my whole life, but I now feel happier than I ever did in my 20s. New York City's accent is dying out, study finds It's something that's fixed from roughly the age of 14. But your accent could be hindering your job prospects, according to a new study.

A US physicist and a Canadian computer scientist have won this year's Turing Award for their invention of a form of seemingly unbreakable encryption. Charles H Bennett and Gilles Brassard's work, which dates back to 1984, is known as quantum cryptography and has redefined secure communication and computing, the award's body said. Scientists believe their work will be central to electronic communications in a world that depends heavily on data-sharing, but which for years has been trying to develop more powerful quantum computers. The Turing Award, named after the mathematician and code-breaker Alan Turing, is known as the Nobel Prize of computing. It comes with a $1m (£800,000) prize.

Training models with longer in-context lengths is a significant challenge for multimodal machine learning due to substantial GPU memory and computational costs. This exploratory study does not present state-of-the-art models; rather, it introduces an innovative method designed to increase in-context text length in multi-modality large language models (MLLMs) efficiently. We present \ModelFullName (\ModelName), which processes long in-context text using visual tokens. This technique significantly reduces GPU memory usage and floating point operations (FLOPs). For instance, our method expands the pre-training in-context length from 256 to 2048 tokens with fewer FLOPs for a 56 billion parameter MOE model. Experimental results demonstrate that \ModelName enhances OCR capabilities and delivers superior performance on common downstream benchmarks for in-context few-shot evaluation. Additionally, \ModelName proves effective for long context inference, achieving results comparable to full text input while maintaining computational efficiency.

We introduce DataComp for Language Models, a testbed for controlled dataset experiments with the goal of improving language models.As part of DCLM, we provide a standardized corpus of 240T tokens extracted from Common Crawl, effective pretraining recipes based on the OpenLM framework, and a broad suite of 53 downstream evaluations.Participants in the DCLM benchmark can experiment with data curation strategies such as deduplication, filtering, and data mixing atmodel scales ranging from 412M to 7B parameters.As a baseline for DCLM, we conduct extensive experiments and find that model-based filtering is key to assembling a high-quality training set.The resulting dataset, DCLM-Baseline, enables training a 7B parameter language model from scratch to 63% 5-shot accuracy on MMLU with 2T training tokens.Compared to MAP-Neo, the previous state-of-the-art in open-data language models, DCLM-Baseline represents a 6 percentage point improvement on MMLU while being trained with half the compute.Our results highlight the importance of dataset design for training language models and offer a starting point for further research on data curation. We release the \dclm benchmark, framework, models, and datasets at https://www.datacomp.ai/dclm/

The math checks out, so hang on to those jeans. The trend's reliability may be waning as styles continue to diversify, however. Breakthroughs, discoveries, and DIY tips sent six days a week. Clothing trends come and go, but in some cases, they don't stay away for too long. For decades, both the fashion industry and its devotees have referenced the so-called "20-year-rule," which suggests society is liable to see certain styles return at semiregular intervals.