Large Language Models (LLMs) demonstrate substantial potential in delivering legal consultation services to users without a legal background, attributed to their superior text comprehension and generation capabilities. Nonetheless, existing Chinese legal LLMs limit interaction to a single model-user dialogue, unlike the collaborative consultations typical of law firms, where multiple staff members contribute to a single consultation. This limitation prevents an authentic consultation experience. Additionally, extant Chinese legal LLMs suffer from critical limitations: (1) insufficient control over the quality of instruction fine-tuning data; (2) increased model hallucination resulting from users' ambiguous queries; and (3) a reduction in the model's ability to follow instructions over multiple dialogue turns. In response to these challenges, we propose a novel legal dialogue framework that leverages the collaborative capabilities of multiple LLM agents, termed LawLuo. This framework encompasses four agents: a receptionist, a lawyer, a secretary, and a boss, each responsible for different functionalities, collaboratively providing a comprehensive legal consultation to users. Additionally, we constructed two high-quality legal dialogue datasets, KINLED and MURLED, and fine-tuned ChatGLM-3-6b using these datasets. We propose a legal query clarification algorithm called ToLC. Experimental results demonstrate that LawLuo outperforms baseline LLMs, including GPT-4, across three dimensions: lawyer-like language style, the usefulness of legal advice, and the accuracy of legal knowledge. Our code and datasets are available at https://github.com/NEFUJing/LawLuo.

The Israeli military has stepped up near-daily raids on Palestinian cities and towns in the West Bank since the Oct. 7 Hamas-led attack on Israel. Israeli forces have also increasingly carried out deadly airstrikes in the territory, a rise in aerial attacks not seen since the second Palestinian uprising against Israeli occupation in the early 2000s. Before Oct. 7, airstrikes and drone attacks on the West Bank were rare and far more limited than in Gaza. But since the war in Gaza began, Israel has stepped up its aerial attacks on Palestinian areas. The two Palestinian territories have long been under separate governments.

On 16 May 2023, Sam Altman, OpenAI's charming, softly spoken, eternally optimistic billionaire CEO, and I stood in front of the US Senate judiciary subcommittee meeting on AI oversight. We were in Washington DC, and it was at the height of AI mania. Altman, then 38, was the poster boy for it all. Raised in St Louis, Missouri, Altman was the Stanford dropout who had become the president of the massively successful Y Combinator startup incubator before he was 30. A few months before the hearing, his company's product ChatGPT had taken the world by storm. All through the summer of 2023, Altman was treated like a Beatle, stopping by DC as part of a world tour, meeting prime ministers and presidents around the globe. US Senator Kyrsten Sinema gushed: "I've never met anyone as smart as Sam… He's an introvert and shy and humble… But… very good at forming relationships with people on the Hill and… can help folks in government understand AI." Glowing portraits at the time painted the youthful Altman as sincere, talented, rich and interested in nothing more than fostering humanity.

Primarily, there is concern about whether the vast investment in AI byMicrosoft, Google and others will pay off. This has been bubbling away in recent months. Analysts at Goldman Sachs published a note in June with the title "Gen AI: too much spend, too little benefit?" The Wall Street bank asked if a 1tn investment in AI over the next few years will "ever pay off", while an analysis by Sequoia Capital, an early investor in ChatGPT developer OpenAI, estimated that tech companies will need to earn 600bn to pay back their AI investments. Zino says the Magnificent Seven has been hit by these concerns.

In this paper, we attempt to summarize monthly reports as investment reports. Fund managers have a wide range of tasks, one of which is the preparation of investment reports. In addition to preparing monthly reports on fund management, fund managers prepare management reports that summarize these monthly reports every six months or once a year. The preparation of fund reports is a labor-intensive and time-consuming task. Therefore, in this paper, we tackle investment summarization from monthly reports using transformer-based models. There are two main types of summarization methods: extractive summarization and abstractive summarization, and this study constructs both methods and examines which is more useful in summarizing investment reports.

In this study, we propose a methodology to extract, index, and visualize ``climate change narratives'' (stories about the connection between causal and consequential events related to climate change). We use two natural language processing methods, BERT (Bidirectional Encoder Representations from Transformers) and causal extraction, to textually analyze newspaper articles on climate change to extract ``climate change narratives.'' The novelty of the methodology could extract and quantify the causal relationships assumed by the newspaper's writers. Looking at the extracted climate change narratives over time, we find that since 2018, an increasing number of narratives suggest the impact of the development of climate change policy discussion and the implementation of climate change-related policies on corporate behaviors, macroeconomics, and price dynamics. We also observed the recent emergence of narratives focusing on the linkages between climate change-related policies and monetary policy. Furthermore, there is a growing awareness of the negative impacts of natural disasters (e.g., abnormal weather and severe floods) related to climate change on economic activities, and this issue might be perceived as a new challenge for companies and governments. The methodology of this study is expected to be applied to a wide range of fields, as it can analyze causal relationships among various economic topics, including analysis of inflation expectation or monetary policy communication strategy.

This project investigates the efficacy of Large Language Models (LLMs) in understanding and extracting scientific knowledge across specific domains and to create a deep learning framework: Knowledge AI. As a part of this framework, we employ pre-trained models and fine-tune them on datasets in the scientific domain. The models are adapted for four key Natural Language Processing (NLP) tasks: summarization, text generation, question answering, and named entity recognition. Our results indicate that domain-specific fine-tuning significantly enhances model performance in each of these tasks, thereby improving their applicability for scientific contexts. This adaptation enables non-experts to efficiently query and extract information within targeted scientific fields, demonstrating the potential of fine-tuned LLMs as a tool for knowledge discovery in the sciences.

To improve cyber threat analysis practices in cybersecurity, I present a plan to build a formal ontological representation of state actors in cyberspace and of cyber operations. I argue that modelling these phenomena via ontologies allows for coherent integration of data coming from diverse sources, automated reasoning over such data, as well as intelligence extraction and reuse from and of them. Existing ontological tools in cybersecurity can be ameliorated by connecting them to neighboring domains such as law, regulations, governmental institutions, and documents. In this paper, I propose metrics to evaluate currently existing ontological tools to create formal representations in the cybersecurity domain, and I provide a plan to develop and extend them when they are lacking.

Reinforcement learning from human feedback plays a crucial role in aligning language models towards human preferences, traditionally represented through comparisons between pairs or sets of responses within a given context. While many studies have enhanced algorithmic techniques to optimize learning from such data, this work shifts focus to improving preference learning through a data-centric approach. Specifically, we propose enriching existing preference datasets with machine-generated rationales that explain the reasons behind choices. We develop a simple and principled framework to augment current preference learning methods with rationale information. Our comprehensive analysis highlights how rationales enhance learning efficiency. Extensive experiments reveal that rationale-enriched preference learning offers multiple advantages: it improves data efficiency, accelerates convergence to higher-performing models, and reduces verbosity bias and hallucination. Furthermore, this framework is versatile enough to integrate with various preference optimization algorithms. Overall, our findings highlight the potential of re-imagining data design for preference learning, demonstrating that even freely available machine-generated rationales can significantly boost performance across multiple dimensions. The code repository is available at https: //github.com/reds-lab/preference-learning-with-rationales

Former DEA Chief of Operations Ray Donovan joins'America's Newsroom' to discuss Texas Gov. Greg Abbott's warning that cartels are utilizing drones along the southern border. The Mexican army has confirmed that drug cartel-operated bomb-dropping drones have killed soldiers in the western state of Michoacan. Defense Secretary Gen. Luis Cresencio Sandoval did not provide exact figures on the number of casualties suffered in the attacks, according to the Associated Press. Sandoval stated on Friday that attacks targeted patrol units and included over 260 drone-bomb incidents in 2023 alone. "Our personnel have suffered wounds, and some of our troops have even died" in the attacks, Sandoval said.