Multimodal movie genre classification has always been regarded as a demanding multi-label classification task due to the diversity of multimodal data such as posters, plot summaries, trailers and metadata. Although existing works have made great progress in modeling and combining each modality, they still face three issues: 1) unutilized group relations in metadata, 2) unreliable attention allocation, and 3) indiscriminative fused features. Given that the knowledge graph has been proven to contain rich information, we present a novel framework that exploits the knowledge graph from various perspectives to address the above problems. As a preparation, the metadata is processed into a domain knowledge graph. A translate model for knowledge graph embedding is adopted to capture the relations between entities. Firstly we retrieve the relevant embedding from the knowledge graph by utilizing group relations in metadata and then integrate it with other modalities. Next, we introduce an Attention Teacher module for reliable attention allocation based on self-supervised learning. It learns the distribution of the knowledge graph and produces rational attention weights. Finally, a Genre-Centroid Anchored Contrastive Learning module is proposed to strengthen the discriminative ability of fused features. The embedding space of anchors is initialized from the genre entities in the knowledge graph. To verify the effectiveness of our framework, we collect a larger and more challenging dataset named MM-IMDb 2.0 compared with the MM-IMDb dataset. The experimental results on two datasets demonstrate that our model is superior to the state-of-the-art methods. We will release the code in the near future.

Language models are achieving impressive performance on various tasks by aggressively adopting inference-time prompting techniques, such as zero-shot and few-shot prompting. In this work, we introduce EchoPrompt, a simple yet effective approach that prompts the model to rephrase its queries before answering them. EchoPrompt is adapted for both zero-shot and few-shot in-context learning with standard and chain-of-thought prompting. Experimental results show that EchoPrompt yields substantial improvements across all these settings for four families of causal language models. These improvements are observed across various numerical reasoning (e.g. GSM8K, SVAMP), reading comprehension (e.g. DROP), and logical reasoning (e.g. Coin Flipping) tasks. On average, EchoPrompt improves the Zero-shot-CoT performance of code-davinci-002 by 5% in numerical tasks and 13% in reading comprehension tasks. We investigate the factors contributing to EchoPrompt's effectiveness through ablation studies, which reveal that both the original query and the model-generated rephrased version are instrumental in its performance gains. Our empirical results indicate that EchoPrompt is an effective technique that enhances in-context learning performance. We recommend incorporating EchoPrompt into various baseline prompting strategies to achieve performance boosts.

Large Language Models (LLMs) have shown immense potential in multimodal applications, yet the convergence of textual and musical domains remains relatively unexplored. To address this gap, we present MusiLingo, a novel system for music caption generation and music-related query responses. MusiLingo employs a single projection layer to align music representations from the pre-trained frozen music audio model MERT with the frozen Vicuna-7B language model (an adaption of LLaMA), bridging the gap between music audio and textual contexts. We train it on an extensive music caption dataset and fine-tune it with instructional data. Due to the scarcity of high-quality music Q\&A datasets, we created the Music Instruct (MI) dataset from captions in the MusicCaps datasets, tailored for open-ended music inquiries. Empirical evaluations demonstrate its competitive performance in generating music captions and composing music-related Q&A pairs.

The unparalleled performance of closed-sourced ChatGPT has sparked efforts towards its democratization, with notable strides made by leveraging real user and ChatGPT conversations, as evidenced by Vicuna. However, due to challenges in gathering conversations involving human participation, current endeavors like Baize and UltraChat aim to automatically generate conversational data. They primarily rely on ChatGPT conducting roleplay to simulate human behaviors based on instructions rather than genuine learning from humans, resulting in limited scope, diminished diversity, and an absence of genuine multi-round conversational dynamics. To address the above issues, we target human questions extracted from genuine human-machine conversations as a learning goal and train a user simulator called `Socratic' to produce a high-quality human-centric synthetic conversation dataset. Subsequently, this dataset was used to train our assistant model, named `PlatoLM'. Experimentally, PlatoLM outpaces baseline models in both Vicuna-Bench and MT-Bench by pairwise comparison when considering equivalent training set sizes, and manual evaluation also shows that our model is highly competitive. Impressively, when fine-tuned with the latest LLaMA 2 model, PlatoLM achieves the SOTA performance among 7B models (including LLaMA-2-7B-chat and Vicuna-7B) in MT-Bench benchmark and in Alpaca-Eval benchmark, it ranks second among 7B models, even beating some larger scale models (including LLaMA-2-13B-chat and GPT-3.5). Further in-depth analysis demonstrates the scalability and transferability of our approach. The code is available at https://github.com/FreedomIntelligence/PlatoLM.

Researchers commonly perform sentiment analysis on large collections of short texts like tweets, Reddit posts or newspaper headlines that are all focused on a specific topic, theme or event. Usually, general purpose sentiment analysis methods are used which perform well on average but miss the variation in meaning that happens across different contexts, for example, the word "active" has a very different intention and valence in the phrase "active lifestyle" versus "active volcano". This work presents a new approach, CIDER (Context Informed Dictionary and sEntiment Reasoner), which performs context sensitive sentiment analysis, where the valence of sentiment laden terms is inferred from the whole corpus before being used to score the individual texts. In this paper we detail the CIDER algorithm and demonstrate that it outperforms state-of-the-art generalist sentiment analysis on a large collection of tweets about the weather. We have made our implementation of CIDER available as a python package: https://pypi.org/project/ciderpolarity/.

Large language models (LLMs) can achieve highly effective performance on various reasoning tasks by incorporating step-by-step chain-of-thought (CoT) prompting as demonstrations. However, the reasoning chains of demonstrations generated by LLMs are prone to errors, which can subsequently lead to incorrect reasoning during inference. Furthermore, inappropriate exemplars (overly simplistic or complex), can affect overall performance among varying levels of difficulty. We introduce Iter-CoT (Iterative bootstrapping in Chain-of-Thoughts Prompting), an iterative bootstrapping approach for selecting exemplars and generating reasoning chains. By utilizing iterative bootstrapping, our approach enables LLMs to autonomously rectify errors, resulting in more precise and comprehensive reasoning chains. Simultaneously, our approach selects challenging yet answerable questions accompanied by reasoning chains as exemplars with a moderate level of difficulty, which enhances the LLMs' generalizability across varying levels of difficulty. Experimental results indicate that Iter-CoT exhibits superiority, achieving competitive performance across three distinct reasoning tasks on ten datasets.

The psychology of first impressions is how we make snap decisions based on initial impressions, and by using AI people can put themselves in the running to potentially be considered for an opportunity. On the other hand it could affect people's self-worth and beliefs that they themselves are not good enough comparatively to their AI generation resulting in low confidence.

CyberGuy explains the wearable AI Pin worn by Naomi Campbell. Ever wished your shoes had rockets attached for that instant zoom to work? Or dreamt of clothes that could change colors to match your mood? While we may have to wait a bit for those, a new high-tech gadget is here to transform your lapel into a sleek tech companion that can whisper the news, translate languages, take an optical look around or even take calls. Click to get Kurt's free CyberGuy newsletter with security alerts, quick video tips, tech reviews, and easy how-to's to make you smarter The Humane AI Pin is an AI-powered wearable gadget.

Since the original Pixel, the special sauce for all of Google's phones has been its software. We've seen this throughout the years in its cameras with things like HDR processing and Google's potent Night Sight mode. But on the Pixel 8 and Pixel 8 Pro, thanks to the new Tensor G3 chip and focus on machine learning, it feels like Google's latest flagship phones are taking some of the buzz from the recent AI hype cycle and turning it into tools you'll actually want to use. Their corners are a touch more rounded and Google deleted the small chin below the screen by making its bezels a uniform size all around. One notable change is that the Pixel 8 has shrunk a bit to a 6.2-inch screen (down from 6.3 inches on the Pixel 7).

Johnson spends millions every year in order to find a way to make his organs similar to that of an 18-year-old male. 'IT'S HUMANS I FEAR': Tech billionaire on journey to immortality welcomes AI as a solution. NO PRESSURE: IG report calls on VA to fix automated system that led to faulty claims decisions. Radiologist Bhavik Patel, M.D. (pictured here) has been named chief AI officer at Mayo Clinic Arizona. Businessman chatting through chatbot Online customer service with chat bots for support.