Few-shot Chain-of-Thought (CoT) significantly enhances the reasoning capabilities of large language models (LLMs), functioning as a whole to guide these models in generating reasoning steps toward final answers. However, we observe that isolated segments, words, or tokens within CoT demonstrations can unexpectedly disrupt the generation process of LLMs. The model may overly concentrate on certain local information present in the demonstration, introducing irrelevant noise into the reasoning process and potentially leading to incorrect answers. In this paper, we investigate the underlying mechanism of CoT through dynamically tracing and manipulating the inner workings of LLMs at each output step, which demonstrates that tokens exhibiting specific attention characteristics are more likely to induce the model to take things out of context; these tokens directly attend to the hidden states tied with prediction, without substantial integration of non-local information. Building upon these insights, we propose a Few-shot Attention Intervention method (FAI) that dynamically analyzes the attention patterns of demonstrations to accurately identify these tokens and subsequently make targeted adjustments to the attention weights to effectively suppress their distracting effect on LLMs. Comprehensive experiments across multiple benchmarks demonstrate consistent improvements over baseline methods, with a remarkable 5.91% improvement on the AQuA dataset, further highlighting the effectiveness of FAI. The most prevalent paradigm of CoT is known as few-shot CoT, which comprises a handful of demonstrations, each consisting of a query paired with a reasoning chain. However, in practice, the performance of LLMs is sensitive to the selection of CoT demonstrations (Huang et al., 2023; Rubin et al., 2021; Luo et al., 2023; Liu et al., 2023; Su et al., 2022). Employing diverse CoT exemplars can cause considerable variations in the overall precision of LLMs. We further demonstrate that even when overall accuracy rates are comparable, varying CoT demonstrations can lead to substantial differences in the distribution of specific questions that are answered correctly versus those answered incorrectly. Yet the underlying cause of the observed performance variations remains largely unclear. Question: Jenn is saving up money to buy a bike. She has 5 jars full of quarters. Each jar can hold 160 quarters. If Question: Agatha has $60 to spend on a new bike. She Question: Mary has 6 jars of sprinkles in her pantry. Answer: Jenn has 5 * 160 = <<5*160=800>>800 quarters. If each pan holds 12 cupcakes, how many Answer: Agatha spends 15+25=<<15+25=40>>40 dollars.

This article explores the concept of technological singularity and the factors that could accelerate or hinder its arrival. The butterfly effect is used as a framework to understand how seemingly small changes in complex systems can have significant and unpredictable outcomes. In section II, we discuss the various factors that could hasten the arrival of technological singularity, such as advances in artificial intelligence and machine learning, breakthroughs in quantum computing, progress in brain-computer interfaces and human augmentation, and development of nanotechnology and 3D printing. In section III, we examine the factors that could delay or impede the arrival of technological singularity, including technical limitations and setbacks in AI and machine learning, ethical and societal concerns around AI and its impact on jobs and privacy, lack of sufficient investment in research and development, and regulatory barriers and political instability. Section IV explores the interplay of these factors and how they can impact the butterfly effect. Finally, in the conclusion, we summarize the key points discussed and emphasize the importance of considering the butterfly effect in predicting the future of technology. We call for continued research and investment in technology to shape its future and mitigate potential risks.

The authors propose an approach to increase the representation power of neural network by introducing communication between the neurons in the same layer. To this end a neural communication bloc is introduced. It first encodes the feature map of each neuron to reduce its dimensionality by a factor of 8. Then an attention-based GCN is used to propagate the information between the neurons via a fully-connected graph. In practice, a weighted sum of the neuron encodings is computed for each node, where the weights are determined by the nodes' features similarity. Finally, the updated representation is decoded to the original resolution and added to the original features. Importantly, this model applies the same operations to every neuron, thus the number of parameters is independent of the feature dimensionality, but dependent on the spatial size of the feature map.

In the recent past, there were several works on the prediction of stock price using different methods. Sentiment analysis of news and tweets and relating them to the movement of stock prices have already been explored. But, when we talk about the news, there can be several topics such as politics, markets, sports etc. It was observed that most of the prior analyses dealt with news or comments associated with particular stock prices only or the researchers dealt with overall sentiment scores only. However, it is quite possible that different topics having different levels of impact on the movement of the stock price or an index. The current study focused on bridging this gap by analysing the movement of Nifty 50 index with respect to the sentiments associated with news items related to various different topic such as sports, politics, markets etc. The study established that sentiment scores of news items of different other topics also have a significant impact on the movement of the index.

Former National Border Patrol Council President Brandon Judd on border agents threatening to leave if Kamala Harris wins the presidential election and explains agents' frustrations with the Biden-Harris administration. The Border Patrol is facing a large-scale outage of security cameras at the southern border with a memo reportedly warning it is having "significant impacts" on operations in apprehending migrants, although officials note there are other layers of security in place as well. NBC News reported that an October memo said nearly one-third of cameras, roughly 150 of 500 cameras on surveillance towers, were out due to technical issues. "The nationwide issue is having significant impacts on [Border Patrol] operations," the memo said. The Remote Video Surveillance Systems are nearly 15 years old and are used to monitor areas of the border without the need for regular on the ground patrols.

In evaluating the long-context capabilities of large language models (LLMs), identifying content relevant to a user's query from original long documents is a crucial prerequisite for any LLM to answer questions based on long text. We present NeedleBench, a framework consisting of a series of progressively more challenging tasks for assessing bilingual long-context capabilities, spanning multiple length intervals (4k, 8k, 32k, 128k, 200k, 1000k, and beyond) and different depth ranges, allowing the strategic insertion of critical data points in different text depth zones to rigorously test the retrieval and reasoning capabilities of models in diverse contexts. We use the NeedleBench framework to assess how well the leading open-source models can identify key information relevant to the question and apply that information to reasoning in bilingual long texts. Furthermore, we propose the Ancestral Trace Challenge (ATC) to mimic the complexity of logical reasoning challenges that are likely to be present in real-world long-context tasks, providing a simple method for evaluating LLMs in dealing with complex long-context situations. Our results suggest that current LLMs have significant room for improvement in practical long-context applications, as they struggle with the complexity of logical reasoning challenges that are likely to be present in real-world long-context tasks. All codes and resources are available at OpenCompass: https://github.com/open-compass/opencompass.

CryptoGPT: a 7B model competing with GPT-4 in a specific task -- The Impact of Automatic Annotation and Strategic Fine-Tuning via QLoRAIn this article, we present a method aimed at refining a dedicated LLM of reasonable quality with limited resources in an industrial setting via CryptoGPT. It is an LLM designed for financial news analysis for the cryptocurrency market in real-time. This project was launched in an industrial context. This model allows not only for the classification of financial information but also for providing comprehensive analysis. We refined different LLMs of the same size such as Mistral-7B and LLama-7B using semi-automatic annotation and compared them with various LLMs such as GPT-3.5 and GPT-4. Our goal is to find a balance among several needs: 1. Protecting data (by avoiding their transfer to external servers), 2. Limiting annotation cost and time, 3. Controlling the model's size (to manage deployment costs), and 4. Maintaining better analysis quality.

Language is essentially a complex, intricate system of human expressions governed by grammatical rules. It poses a significant challenge to develop capable AI algorithms for comprehending and grasping a language. As a major approach, language modeling has been widely studied for language understanding and generation in the past two decades, evolving from statistical language models to neural language models. Recently, pre-trained language models (PLMs) have been proposed by pre-training Transformer models over large-scale corpora, showing strong capabilities in solving various NLP tasks. Since researchers have found that model scaling can lead to performance improvement, they further study the scaling effect by increasing the model size to an even larger size. Interestingly, when the parameter scale exceeds a certain level, these enlarged language models not only achieve a significant performance improvement but also show some special abilities that are not present in small-scale language models. To discriminate the difference in parameter scale, the research community has coined the term large language models (LLM) for the PLMs of significant size. Recently, the research on LLMs has been largely advanced by both academia and industry, and a remarkable progress is the launch of ChatGPT, which has attracted widespread attention from society. The technical evolution of LLMs has been making an important impact on the entire AI community, which would revolutionize the way how we develop and use AI algorithms. In this survey, we review the recent advances of LLMs by introducing the background, key findings, and mainstream techniques. In particular, we focus on four major aspects of LLMs, namely pre-training, adaptation tuning, utilization, and capacity evaluation. Besides, we also summarize the available resources for developing LLMs and discuss the remaining issues for future directions.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Using AI technology to read mammograms and assist in making diagnoses could put patients at risk, a new study is revealing. Often touted as a "second set of eyes" for radiologists, AI-based mammographic support systems are "extremely promising," said news agency SWNS. But as the technology grows and expands, there are concerns among some that it may make radiologists "favor the AI's suggestion over their own," the agency added.

In 1971, three student teachers at Carleton College in Minnesota created a computer game to teach their students about the westward expansion. Don Rawitsch, Bill Heinemann, and Paul Dillenberger programmed the game in BASIC language on an HP 2100 minicomputer with only 32 kilobytes of memory. The game was designed to simulate the experience of a family traveling from Missouri to Oregon in 1848 and teach students about the challenges faced by pioneers on the Oregon Trail. The game became popular in classrooms across the United States and eventually was published by MECC (Minnesota Educational Computing Consortium) in 1985. Over the years, "The Oregon Trail" has undergone numerous updates and re-releases for various platforms. The original version of the game was text-based, and players had to use the arrow keys to navigate their wagon.