Sentiment analysis is a pivotal task in the domain of natural language processing. It encompasses both text-level sentiment polarity classification and word-level Part of Speech(POS) sentiment polarity determination. Such analysis challenges models to understand text holistically while also extracting nuanced information. With the rise of Large Language Models(LLMs), new avenues for sentiment analysis have opened. This paper proposes enhancing performance by leveraging the Mutual Reinforcement Effect(MRE) between individual words and the overall text. It delves into how word polarity influences the overarching sentiment of a passage. To support our research, we annotated four novel Sentiment Text Classification and Part of Speech(SCPOS) datasets, building upon existing sentiment classification datasets. Furthermore, we developed a Universal Sentiment Analysis(USA) model, with a 7-billion parameter size. Experimental results revealed that our model surpassed the performance of gpt-3.5-turbo across all four datasets, underscoring the significance of MRE in sentiment analysis.

This paper investigates a challenging problem of zero-shot learning in the multi-label scenario (MLZSL), wherein, the model is trained to recognize multiple unseen classes within a sample (e.g., an image) based on seen classes and auxiliary knowledge, e.g., semantic information. Existing methods usually resort to analyzing the relationship of various seen classes residing in a sample from the dimension of spatial or semantic characteristics, and transfer the learned model to unseen ones. But they ignore the effective integration of local and global features. That is, in the process of inferring unseen classes, global features represent the principal direction of the image in the feature space, while local features should maintain uniqueness within a certain range. This integrated neglect will make the model lose its grasp of the main components of the image. Relying only on the local existence of seen classes during the inference stage introduces unavoidable bias. In this paper, we propose a novel and effective group bi-enhancement framework for MLZSL, dubbed GBE-MLZSL, to fully make use of such properties and enable a more accurate and robust visual-semantic projection. Specifically, we split the feature maps into several feature groups, of which each feature group can be trained independently with the Local Information Distinguishing Module (LID) to ensure uniqueness. Meanwhile, a Global Enhancement Module (GEM) is designed to preserve the principal direction. Besides, a static graph structure is designed to construct the correlation of local features. Experiments on large-scale MLZSL benchmark datasets NUS-WIDE and Open-Images-v4 demonstrate that the proposed GBE-MLZSL outperforms other state-of-the-art methods with large margins.

In this paper, we create YORC: a new multi-choice Yoruba Reading Comprehension dataset that is based on Yoruba high-school reading comprehension examination. We provide baseline results by performing cross-lingual transfer using existing English RACE dataset based on a pre-trained encoder-only model. Additionally, we provide results by prompting large language models (LLMs) like GPT-4.

Simple and effective interaction between human and quadrupedal robots paves the way towards creating intelligent and capable helper robots, forging a future where technology enhances our lives in ways beyond our imagination [1, 2, 3]. Key to such human-robot interaction system is enabling quadrupedal robots to respond to natural language instructions as language is one of the most important communication channels for human beings. Recent developments in Large Language Models (LLMs) have engendered a spectrum of applications that were once considered unachievable, including virtual assistance [4], code generation [5], translation [6], and logical reasoning [7], fueled by the proficiency of LLMs to ingest an enormous amount of historical data, to adapt in-context to novel tasks with few examples, and to understand and interact with user intentions through a natural language interface. The burgeoning success of LLMs has also kindled interest within the robotics researcher community, with an aim to develop interactive and capable systems for physical robots [8, 9, 10, 11, 12, 13]. Researchers have demonstrated the potential of using LLMs to perform high-level planning [8, 9], and robot code writing [11, 13]. Nevertheless, unlike text generation where LLMs directly interpret the atomic elements--tokens--it often proves challenging for LLMs to comprehend low-level robotic commands such as joint angle targets or motor torques, especially for inherently unstable legged robots necessitating high-frequency control signals. Consequently, most existing work presume the provision of high-level APIs for LLMs to dictate robot behaviour, inherently limiting the system's expressive capabilities. We address this limitation by using foot contact patterns as an interface that bridges human instructions in natural language and low-level commands.

Knowledge Graphs (KGs) often have two characteristics: heterogeneous graph structure and text-rich entity/relation information. Text-based KG embeddings can represent entities by encoding descriptions with pre-trained language models, but no open-sourced library is specifically designed for KGs with PLMs at present. In this paper, we present LambdaKG, a library for KGE that equips with many pre-trained language models (e.g., BERT, BART, T5, GPT-3), and supports various tasks (e.g., knowledge graph completion, question answering, recommendation, and knowledge probing). LambdaKG is publicly open-sourced at https://github.com/zjunlp/PromptKG/tree/main/lambdaKG, with a demo video at http://deepke.zjukg.cn/lambdakg.mp4 and long-term maintenance.

Large language models (LLMs) have played a pivotal role in revolutionizing various facets of our daily existence. Solving attention regression is a fundamental task in optimizing LLMs. In this work, we focus on giving a provable guarantee for the one-layer attention network objective function $L(X,Y) = \sum_{j_0 = 1}^n \sum_{i_0 = 1}^d ( \langle \langle \exp( \mathsf{A}_{j_0} x ) , {\bf 1}_n \rangle^{-1} \exp( \mathsf{A}_{j_0} x ), A_{3} Y_{*,i_0} \rangle - b_{j_0,i_0} )^2$. Here $\mathsf{A} \in \mathbb{R}^{n^2 \times d^2}$ is Kronecker product between $A_1 \in \mathbb{R}^{n \times d}$ and $A_2 \in \mathbb{R}^{n \times d}$. $A_3$ is a matrix in $\mathbb{R}^{n \times d}$, $\mathsf{A}_{j_0} \in \mathbb{R}^{n \times d^2}$ is the $j_0$-th block of $\mathsf{A}$. The $X, Y \in \mathbb{R}^{d \times d}$ are variables we want to learn. $B \in \mathbb{R}^{n \times d}$ and $b_{j_0,i_0} \in \mathbb{R}$ is one entry at $j_0$-th row and $i_0$-th column of $B$, $Y_{*,i_0} \in \mathbb{R}^d$ is the $i_0$-column vector of $Y$, and $x \in \mathbb{R}^{d^2}$ is the vectorization of $X$. In a multi-layer LLM network, the matrix $B \in \mathbb{R}^{n \times d}$ can be viewed as the output of a layer, and $A_1= A_2 = A_3 \in \mathbb{R}^{n \times d}$ can be viewed as the input of a layer. The matrix version of $x$ can be viewed as $QK^\top$ and $Y$ can be viewed as $V$. We provide an iterative greedy algorithm to train loss function $L(X,Y)$ up $\epsilon$ that runs in $\widetilde{O}( ({\cal T}_{\mathrm{mat}}(n,n,d) + {\cal T}_{\mathrm{mat}}(n,d,d) + d^{2\omega}) \log(1/\epsilon) )$ time. Here ${\cal T}_{\mathrm{mat}}(a,b,c)$ denotes the time of multiplying $a \times b$ matrix another $b \times c$ matrix, and $\omega\approx 2.37$ denotes the exponent of matrix multiplication.

More than 20 tech and civil society leaders, including the chief executives of five of the 10 biggest U.S. companies, appeared at a closed-door Senate meeting on Wednesday to shape how artificial intelligence is regulated. The meeting, which was organized by Senate Majority Leader Chuck Schumer, included a prestigious, and possibly combustible, mix of personalities with diverging views on how to write the rules for AI. The CEOs of Alphabet, Microsoft, Meta Platforms and OpenAI were invited to appear alongside rivals and industry critics to discuss possible guardrails for AI that balance the risks and rewards of the technology. Areas of disagreement were apparent throughout the morning session, according to several people who were in the room. Meta CEO Mark Zuckerberg, OpenAI CEO Sam Altman and Microsoft co-founder Bill Gates offered diverging views on the risks of open-source AI research, according to people in the room.

Chatbots could be used to diagnose patients in a bid to reduce waiting times in emergency departments, researchers have indicated. It comes after a study found that ChatGPT, powered by artificial intelligence (AI), 'performed well' in generating a list of diagnoses for patients and suggesting the most likely option. Researchers in the Netherlands entered the records of 30 patients who visited an emergency department in 2022, as well as anonymous doctors' notes, into ChatGPT versions 3.5 and 4.0. The AI analysis was compared to two clinicians who made a diagnosis based on the same information, both with and without laboratory data. When lab data was included, doctors had the correct answer in their top five differential diagnoses in 87% of cases, compared with 97% for ChatGPT 3.5 and 87% for ChatGPT 4.0.

Amazon is launching a new AI tool that generates product listings for sellers. The feature uses a large language model (LLM) "trained on large amounts of data" to make it faster and simpler for vendors to describe their products. The company describes the tool as distilling the "significant work" of creating titles, bullet points and descriptions down to "just one step." Amazon says its Generative Listing Content tool only requires sellers to provide a brief product description in a few words or sentences. From there, it will "generate high-quality content for their review" -- including a title, product description and bullet points -- which sellers can peruse before refining or submitting as is.

Senate Majority Leader Charles E. Schumer (D-N.Y.) will host the AI Insight Forum -- which is intended to serve as the bedrock for his "all hands on deck" plan to respond to recent AI advances -- in the grand Kennedy Caucus Room, the historic stage of Senate probes into the sinking of the Titanic, as well as Watergate. The more than 20 attendees include Tesla CEO and X owner Elon Musk, Meta CEO Mark Zuckerberg, Google CEO Sundar Pichai and ChatGPT-maker OpenAI CEO Sam Altman, among other top tech executives, civil rights leaders, labor chiefs and researchers.