The rapid growth of the financial sector and the rising focus on Environmental, Social, and Governance (ESG) considerations highlight the need for advanced NLP tools. However, open-source LLMs proficient in both finance and ESG domains remain scarce. To address this gap, we introduce SusGen-30K, a category-balanced dataset comprising seven financial NLP tasks and ESG report generation, and propose TCFD-Bench, a benchmark for evaluating sustainability report generation. Leveraging this dataset, we developed SusGen-GPT, a suite of models achieving state-of-the-art performance across six adapted and two off-the-shelf tasks, trailing GPT-4 by only 2% despite using 7-8B parameters compared to GPT-4's 1,700B. Based on this, we propose the SusGen system, integrated with Retrieval-Augmented Generation (RAG), to assist in sustainability report generation. This work demonstrates the efficiency of our approach, advancing research in finance and ESG.

In recent years, the rise of AI-assisted code-generation tools has significantly transformed software development. While code generators have mainly been used to support conventional software development, their use will be extended to powerful and secure AI systems. Systems capable of generating code, such as ChatGPT, OpenAI Codex, GitHub Copilot, and AlphaCode, take advantage of advances in machine learning (ML) and natural language processing (NLP) enabled by large language models (LLMs). However, it must be borne in mind that these models work probabilistically, which means that although they can generate complex code from natural language input, there is no guarantee for the functionality and security of the generated code. However, to fully exploit the considerable potential of this technology, the security, reliability, functionality, and quality of the generated code must be guaranteed. This paper examines the implementation of these goals to date and explores strategies to optimize them. In addition, we explore how these systems can be optimized to create safe, high-performance, and executable artificial intelligence (AI) models, and consider how to improve their accessibility to make AI development more inclusive and equitable.

Chinese Named Entity Recognition (NER) is an important task in information extraction, which has a significant impact on downstream applications. Due to the lack of natural separators in Chinese, previous NER methods mostly relied on external dictionaries to enrich the semantic and boundary information of Chinese words. However, such methods may introduce noise that affects the accuracy of named entity recognition. To this end, we propose a character relation enhanced Chinese NER model (CRENER). This model defines four types of tags that reflect the relationships between characters, and proposes a fine-grained modeling of the relationships between characters based on three types of relationships: adjacency relations between characters, relations between characters and tags, and relations between tags, to more accurately identify entity boundaries and improve Chinese NER accuracy. Specifically, we transform the Chinese NER task into a character-character relationship classification task, ensuring the accuracy of entity boundary recognition through joint modeling of relation tags. To enhance the model's ability to understand contextual information, WRENER further constructed an adapted transformer encoder that combines unscaled direction-aware and distance-aware masked self-attention mechanisms. Moreover, a relationship representation enhancement module was constructed to model predefined relationship tags, effectively mining the relationship representations between characters and tags. Experiments conducted on four well-known Chinese NER benchmark datasets have shown that the proposed model outperforms state-of-the-art baselines. The ablation experiment also demonstrated the effectiveness of the proposed model.

OpenAI published receipts, in the form of a long timeline of emails, texts and legal filings, illustrating that Elon Musk's injunction to prevent OpenAI from converting into a for-profit company runs counter to what he wanted in 2017. Essentially, OpenAI is providing even more evidence to the fact that its former co-founder wanted the AI startup to become a for-profit company and make him CEO. You should read the whole blog to get all of the details (and get a sense for how billionaires email) but the gist is that in 2017, Musk and OpenAI came to an understanding that the then non-profit needed to become a for-profit to "advance its mission" and seemingly capitalize on the public interest earned from its AI beating professional Dota 2 players in one-on-one matches. According to OpenAI, Musk proposed a new board structure where he "would unequivocally have initial control of the company," which OpenAI was opposed to. That led to the disagreements between Musk and OpenAI leadership, and him ultimately leaving the nonprofit's board in 2018.

OpenAI CEO Sam Altman expects AGI, or artificial general intelligence--AI that outperforms humans at most tasks--around 2027 or 2028. Elon Musk's prediction is either 2025 or 2026, and he has claimed that he was "losing sleep over the threat of AI danger." As the limitations of current AI become increasingly clear, most AI researchers have come to the view that simply building bigger and more powerful chatbots won't lead to AGI. This story is from the WIRED World in 2025, our annual trends briefing. However, in 2025, AI will still pose a massive risk: not from artificial superintelligence, but from human misuse.

New Jersey residents frustrated with a lack of answers regarding dozens of potential drone sightings in the skies above their homes are threatening to take action on their own if the government doesn't start providing answers. James Ward, a Jersey Shore Realtor, shared video on Facebook that he said shows "SUV-size drones" above Island Beach State Park taken Sunday. It's difficult to judge their size in the clip, which shows a number of lights hovering in the sky. "Dozens of SUV-size drones in all directions," the caption says. "Emerging at same time and flying over the ocean and then heading in different directions – what do you think?" "A good shotgun will fix that problem," one commenter replied.

The security of Large Language Models (LLMs) has become an important research topic since the emergence of ChatGPT. Though there have been various effective methods to defend against jailbreak attacks, prefilling attacks remain an unsolved and popular threat against open-sourced LLMs. In-Context Learning (ICL) offers a computationally efficient defense against various jailbreak attacks, yet no effective ICL methods have been developed to counter prefilling attacks. In this paper, we: (1) show that ICL can effectively defend against prefilling jailbreak attacks by employing adversative sentence structures within demonstrations; (2) characterize the effectiveness of this defense through the lens of model size, number of demonstrations, over-defense, integration with other jailbreak attacks, and the presence of safety alignment. Given the experimental results and our analysis, we conclude that there is no free lunch for defending against prefilling jailbreak attacks with ICL. On the one hand, current safety alignment methods fail to mitigate prefilling jailbreak attacks, but adversative structures within ICL demonstrations provide robust defense across various model sizes and complex jailbreak attacks. On the other hand, LLMs exhibit similar over-defensiveness when utilizing ICL demonstrations with adversative structures, and this behavior appears to be independent of model size.

Workshop Goals This workshop aimed to identify initial challenges and opportunities for national resources for AI research (e.g., compute, data, models, etc.) and to facilitate planning for the envisioned National AI Research Resource (NAIRR). Participants included AI and cyberinfrastructure (CI) experts. Significant Findings 1. AI researchers confront unprecedented scale that goes well beyond generative AI 2. National investments in AI research resources have been insufficient 3. The suboptimal usability of current resources is compromising AI investigation topics 4. The cadence and intensity of AI conference publications is unlike other research areas 5. Better practices for managing local resources are needed 6. Access to AI research resources is very uneven for different institutions 7. There is an opportunity for greater alignment between CI and AI efforts 8. AI research needs warrant unique approaches to CI and to national shared resources Critical Needs Participants identified ten prototypical AI workflows in two major areas with an immediate need for large-scale resources.

Recent regulatory proposals for artificial intelligence emphasize fairness requirements for machine learning models. However, precisely defining the appropriate measure of fairness is challenging due to philosophical, cultural and political contexts. Biases can infiltrate machine learning models in complex ways depending on the model's context, rendering a single common metric of fairness insufficient. This ambiguity highlights the need for criteria to guide the selection of context-aware measures, an issue of increasing importance given the proliferation of ever tighter regulatory requirements. To address this, we developed a flowchart to guide the selection of contextually appropriate fairness measures. Twelve criteria were used to formulate the flowchart. This included consideration of model assessment criteria, model selection criteria, and data bias. We also review fairness literature in the context of machine learning and link it to core regulatory instruments to assist policymakers, AI developers, researchers, and other stakeholders in appropriately addressing fairness concerns and complying with relevant regulatory requirements.

The proliferation of disinformation presents a growing threat to societal trust and democracy, necessitating robust and scalable Fact-Checking systems. In this work, we present Dynamic Evidence-based FAct-checking with Multimodal Experts (DEFAME), a modular, zero-shot MLLM pipeline for open-domain, text-image claim verification. DEFAME frames the problem of fact-checking as a six-stage process, dynamically deciding about the usage of external tools for the retrieval of textual and visual evidence. In addition to the claim's veracity, DEFAME returns a justification accompanied by a comprehensive, multimodal fact-checking report. While most alternatives either focus on sub-tasks of fact-checking, lack explainability or are limited to text-only inputs, DEFAME solves the problem of fact-checking end-to-end, including claims with images or those that require visual evidence. Evaluation on the popular benchmarks VERITE, AVeriTeC, and MOCHEG shows that DEFAME surpasses all previous methods, establishing it as the new state-of-the-art fact-checking system.