With the proliferation of data across various domains, there is a critical demand for tools that enable non-experts to derive meaningful insights without deep data analysis skills. To address this need, existing automatic fact sheet generation tools offer heuristic-based solutions to extract facts and generate stories. However, they inadequately grasp the semantics of data and struggle to generate narratives that fully capture the semantics of the dataset or align the fact sheet with specific user needs. Addressing these shortcomings, this paper introduces \tool, a novel tool designed for the automatic generation and customisation of fact sheets. \tool applies the concept of collaborative AI workers to transform raw tabular dataset into comprehensive, visually compelling fact sheets. We define effective taxonomy to profile AI worker for specialised tasks. Furthermore, \tool empowers users to refine these fact sheets through intuitive natural language commands, ensuring the final outputs align closely with individual preferences and requirements. Our user evaluation with 18 participants confirms that \tool not only surpasses state-of-the-art baselines in automated fact sheet production but also provides a positive user experience during customization tasks.

Countless decisions shape our daily lives, and it is paramount to understand the how and why behind these choices. In this paper, we introduce a new LLM decision-making framework called STRUX, which enhances LLM decision-making by providing structured explanations. These include favorable and adverse facts related to the decision, along with their respective strengths. STRUX begins by distilling lengthy information into a concise table of key facts. It then employs a series of self-reflection steps to determine which of these facts are pivotal, categorizing them as either favorable or adverse in relation to a specific decision. Lastly, we fine-tune an LLM to identify and prioritize these key facts to optimize decision-making. STRUX has been evaluated on the challenging task of forecasting stock investment decisions based on earnings call transcripts and demonstrated superior performance against strong baselines. It enhances decision transparency by allowing users to understand the impact of different factors, representing a meaningful step towards practical decision-making with LLMs.

Recent research demonstrates the effectiveness of using pre-trained language models for legal case retrieval. Most of the existing works focus on improving the representation ability for the contextualized embedding of the [CLS] token and calculate relevance using textual semantic similarity. However, in the legal domain, textual semantic similarity does not always imply that the cases are relevant enough. Instead, relevance in legal cases primarily depends on the similarity of key facts that impact the final judgment. Without proper treatments, the discriminative ability of learned representations could be limited since legal cases are lengthy and contain numerous non-key facts. To this end, we introduce DELTA, a discriminative model designed for legal case retrieval. The basic idea involves pinpointing key facts in legal cases and pulling the contextualized embedding of the [CLS] token closer to the key facts while pushing away from the non-key facts, which can warm up the case embedding space in an unsupervised manner. To be specific, this study brings the word alignment mechanism to the contextual masked auto-encoder. First, we leverage shallow decoders to create information bottlenecks, aiming to enhance the representation ability. Second, we employ the deep decoder to enable translation between different structures, with the goal of pinpointing key facts to enhance discriminative ability. Comprehensive experiments conducted on publicly available legal benchmarks show that our approach can outperform existing state-of-the-art methods in legal case retrieval. It provides a new perspective on the in-depth understanding and processing of legal case documents.

Automatic summarization of mass-emergency events plays a critical role in disaster management. The second edition of CrisisFACTS aims to advance disaster summarization based on multi-stream fact-finding with a focus on web sources such as Twitter, Reddit, Facebook, and Webnews. Here, participants are asked to develop systems that can extract key facts from several disaster-related events, which ultimately serve as a summary. This paper describes our method to tackle this challenging task. We follow previous work and propose to use a combination of retrieval, reranking, and an embarrassingly simple instruction-following summarization. The two-stage retrieval pipeline relies on BM25 and MonoT5, while the summarizer module is based on the open-source Large Language Model (LLM) LLaMA-13b. For summarization, we explore a Question Answering (QA)-motivated prompting approach and find the evidence useful for extracting query-relevant facts. The automatic metrics and human evaluation show strong results but also highlight the gap between open-source and proprietary systems.

Recent generative approaches for multi-hop question answering (QA) utilize the fusion-in-decoder method~\cite{izacard-grave-2021-leveraging} to generate a single sequence output which includes both a final answer and a reasoning path taken to arrive at that answer, such as passage titles and key facts from those passages. While such models can lead to better interpretability and high quantitative scores, they often have difficulty accurately identifying the passages corresponding to key entities in the context, resulting in incorrect passage hops and a lack of faithfulness in the reasoning path. To address this, we propose a single-sequence prediction method over a local reasoning graph (\model)\footnote{Code/Models will be released at \url{https://github.com/gowtham1997/SeqGraph}} that integrates a graph structure connecting key entities in each context passage to relevant subsequent passages for each question. We use a graph neural network to encode this graph structure and fuse the resulting representations into the entity representations of the model. Our experiments show significant improvements in answer exact-match/F1 scores and faithfulness of grounding in the reasoning path on the HotpotQA dataset and achieve state-of-the-art numbers on the Musique dataset with only up to a 4\% increase in model parameters.

Long-form numerical reasoning in financial analysis aims to generate a reasoning program to calculate the correct answer for a given question. Previous work followed a retriever-generator framework, where the retriever selects key facts from a long-form document, and the generator generates a reasoning program based on retrieved facts. However, they treated all facts equally without considering the different contributions of facts with and without numbers. Meanwhile, the program consistency were ignored under supervised training, resulting in lower training accuracy and diversity. To solve these problems, we proposed APOLLO to improve the long-form numerical reasoning framework. For the retriever, we adopt a number-aware negative sampling strategy to enable the retriever to be more discriminative on key numerical facts. For the generator, we design consistency-based reinforcement learning and target program augmentation strategy based on the consistency of program execution results. Experimental results on the FinQA and ConvFinQA leaderboard verify the effectiveness of our proposed method, achieving the new state-of-the-art.

Artificial intelligence stats and facts show just how much impact the AI market has had in the world. It has permanently shifted the technological landscape over the last few years. However, the industry as a whole is still relatively new. As it continues to rise, AI and machine learning will drastically shape the way companies, businesses, and people interact with each other. Here are a few facts and statistics that you should know. Regardless of whether you're a company executive, a warehouse employee, or even just a smartphone owner, it's easy to see the impact that artificial intelligence has on our daily lives.

Matrix AI Network leverages the latest AI technology to deliver on the promise of blockchain.

Why publish a list of our picks for the best new cars that are the safest? Don't confuse "safe" with "safer." Manufacturers make vehicles that are safer than those from 10 years ago, for sure. However, some are safer than others. Both organizations put new car models through a battery of crash and safety tests, scoring each for the degree of protection they provide for occupants. If you choose a car on this list, you can be assured you will likely survive a crash, but in many cases avoid it altogether. We pulled together a collection of the best 2022 models made the safest for you to drive and what earns them that distinction. In a nutshell, these car models go above and beyond government-mandated safety features and manufacturer norms. Read on to learn more. What we looked for were cars with perfect scores in both IIHS and NHTSA testing. With those in hand, we narrowed the field among the trim levels within each model based on standard and available active safety features such as forward collision warning with automatic emergency braking. Several safety features we've grown accustomed to are actually government-mandated. In other words, the federal government made them standard by law. These include antilock brakes, stability control, traction control, rearview cameras, tire pressure monitors, and so forth.

In recent years, the prevalence of artificial intelligence in our everyday lives has increased drastically. We now see such technology in our phones, in cybersecurity, and even in cars. But where did it all begin for AI, and what lies in its future? Well, here are some interesting facts you may not know about artificial intelligence. While the ancient Greeks wrote about "intelligent robots" in religious mythology, artificial intelligence was first conceptualized by Gottfried Wilhelm Leibniz, a German mathematician and philosopher, in the late seventeenth century.