Lately, propelled by the phenomenal advances around the transformer architecture, the legal NLP field has enjoyed spectacular growth. To measure progress, well curated and challenging benchmarks are crucial. However, most benchmarks are English only and in legal NLP specifically there is no multilingual benchmark available yet. Additionally, many benchmarks are saturated, with the best models clearly outperforming the best humans and achieving near perfect scores. We survey the legal NLP literature and select 11 datasets covering 24 languages, creating LEXTREME. To provide a fair comparison, we propose two aggregate scores, one based on the datasets and one on the languages. The best baseline (XLM-R large) achieves both a dataset aggregate score a language aggregate score of 61.3. This indicates that LEXTREME is still very challenging and leaves ample room for improvement. To make it easy for researchers and practitioners to use, we release LEXTREME on huggingface together with all the code required to evaluate models and a public Weights and Biases project with all the runs.

From algorithms firing staff without human intervention to software keeping tabs on bathroom breaks, technologies including artificial intelligence are already upsetting workers and unsettling workplaces. At call centers, AI systems record and grade how workers handle calls, often giving failing grades for not sticking to the script. Some corporate software spies on workers to see whether they ever write the word "union" in their emails. As technologies grow ever more sophisticated in monitoring, surveilling and speeding up workers, many workplace experts say US businesses, labor unions and government are not doing nearly enough to protect workers from tech's downsides. "Workers are being constantly monitored, and AI-based monitoring tools can make mistakes that can translate into unfair pay cuts or firings," said Virginia Doellgast, a professor of employment relations at Cornell.

This story was originally published by Wired and is reproduced here as part of the Climate Desk collaboration. For the first time in four centuries, it's good to be a beaver. Long persecuted for their pelts and reviled as pests, the dam-building rodents are today hailed by scientists as ecological saviors. Their ponds and wetlands store water in the face of drought, filter out pollutants, furnish habitat for endangered species, and fight wildfires. In California, Castor canadensis is so prized that the state recently committed millions to its restoration.

The commercialization of diffusion models, renowned for their ability to generate high-quality images that are often indistinguishable from real ones, brings forth potential copyright concerns. Although attempts have been made to impede unauthorized access to copyrighted material during training and to subsequently prevent DMs from generating copyrighted images, the effectiveness of these solutions remains unverified. This study explores the vulnerabilities associated with copyright protection in DMs by introducing a backdoor data poisoning attack (SilentBadDiffusion) against text-to-image diffusion models. Our attack method operates without requiring access to or control over the diffusion model's training or fine-tuning processes; it merely involves the insertion of poisoning data into the clean training dataset. This data, comprising poisoning images equipped with prompts, is generated by leveraging the powerful capabilities of multimodal large language models and text-guided image inpainting techniques. Our experimental results and analysis confirm the method's effectiveness. By integrating a minor portion of non-copyright-infringing stealthy poisoning data into the clean dataset-rendering it free from suspicion-we can prompt the finetuned diffusion models to produce copyrighted content when activated by specific trigger prompts. These findings underline potential pitfalls in the prevailing copyright protection strategies and underscore the necessity for increased scrutiny and preventative measures against the misuse of DMs.

The Competition on Legal Information Extraction/Entailment (COLIEE) is held annually to encourage advancements in the automatic processing of legal texts. Processing legal documents is challenging due to the intricate structure and meaning of legal language. In this paper, we outline our strategies for tackling Task 2, Task 3, and Task 4 in the COLIEE 2023 competition. Our approach involved utilizing appropriate state-of-the-art deep learning methods, designing methods based on domain characteristics observation, and applying meticulous engineering practices and methodologies to the competition. As a result, our performance in these tasks has been outstanding, with first places in Task 2 and Task 3, and promising results in Task 4. Our source code is available at https://github.com/Nguyen2015/CAPTAIN-COLIEE2023/tree/coliee2023.

Intelligent agents stand out as a potential path toward artificial general intelligence (AGI). Thus, researchers have dedicated significant effort to diverse implementations for them. Benefiting from recent progress in large language models (LLMs), LLM-based agents that use universal natural language as an interface exhibit robust generalization capabilities across various applications -- from serving as autonomous general-purpose task assistants to applications in coding, social, and economic domains, LLM-based agents offer extensive exploration opportunities. This paper surveys current research to provide an in-depth overview of LLM-based intelligent agents within single-agent and multi-agent systems. It covers their definitions, research frameworks, and foundational components such as their composition, cognitive and planning methods, tool utilization, and responses to environmental feedback. We also delve into the mechanisms of deploying LLM-based agents in multi-agent systems, including multi-role collaboration, message passing, and strategies to alleviate communication issues between agents. The discussions also shed light on popular datasets and application scenarios. We conclude by envisioning prospects for LLM-based agents, considering the evolving landscape of AI and natural language processing.

The increasing use of transformer-based large language models brings forward the challenge of processing long sequences. In document visual question answering (DocVQA), leading methods focus on the single-page setting, while documents can span hundreds of pages. We present GRAM, a method that seamlessly extends pre-trained single-page models to the multi-page setting, without requiring computationally-heavy pretraining. To do so, we leverage a single-page encoder for local page-level understanding, and enhance it with document-level designated layers and learnable tokens, facilitating the flow of information across pages for global reasoning. To enforce our model to utilize the newly introduced document-level tokens, we propose a tailored bias adaptation method. For additional computational savings during decoding, we introduce an optional compression stage using our C-Former model, which reduces the encoded sequence length, thereby allowing a tradeoff between quality and latency. Extensive experiments showcase GRAM's state-of-the-art performance on the benchmarks for multi-page DocVQA, demonstrating the effectiveness of our approach.

The book, titled'The Algorithm', has pulled the current on how the hiring world is becoming a'Wild West' where unregulated AI algorithms make decisions without human oversight AI has taken over the job market by reading resumes and watching interviews to provide human executives with the best candidates, a new book has revealed. The book, titled'The Algorithm,' has pulled the curtain on how the hiring world is becoming a'Wild West' where unregulated AI algorithms make decisions without human oversight. Artificial intelligence decides who gets hired and who gets fired by monitoring everything from what people post on social media to their tone of voice in interviews, the book's author, Hilke Schellmann, told DailyMail.com. Algorithms can now dictate not only who gets job interviews - but, thanks to continuous on-the-job monitoring, who gets promoted or fired (and they might even warn your boss if you are getting divorced). Schellmann said the CEO of ZipRecruiter told him a few years ago that the tech was screening at least 75 percent of resumes.

In November, a group of non-fiction authors filed a lawsuit accusing OpenAI and Microsoft of using other people's intellectual property without permission to train the former's generative AI technology. Now, more non-fiction writers are suing the companies for using their work to train OpenAI's GPT large language models (LLM). Professional writers "have limited capital to fund their research" and "typically self-fund their projects," they said in their complaint. The plaintiffs added that the companies could've explored alternative financing options, such as profit sharing, but have "decided to steal" instead. They're seeking up to $150,000 per infringed work in damages, as well as a permanent injunction "to prevent these harms from recurring."

The underground exploitation of large language models (LLMs) for malicious services (i.e., Malla) is witnessing an uptick, amplifying the cyber threat landscape and posing questions about the trustworthiness of LLM technologies. However, there has been little effort to understand this new cybercrime, in terms of its magnitude, impact, and techniques. In this paper, we conduct the first systematic study on 212 real-world Mallas, uncovering their proliferation in underground marketplaces and exposing their operational modalities. Our study discloses the Malla ecosystem, revealing its significant growth and impact on today's public LLM services. Through examining 212 Mallas, we uncovered eight backend LLMs used by Mallas, along with 182 prompts that circumvent the protective measures of public LLM APIs. We further demystify the tactics employed by Mallas, including the abuse of uncensored LLMs and the exploitation of public LLM APIs through jailbreak prompts. Our findings enable a better understanding of the real-world exploitation of LLMs by cybercriminals, offering insights into strategies to counteract this cybercrime.