In the end, she fell in with a songwriting collective, the Tuesday Music Club, through her then-boyfriend Kevin Gilbert. Each week, they'd gather in Pasadena and try to write a complete song before dawn broke. The sessions were recorded by Michael Jackson's producer Bill Bottrell for "about two cents" and released as Crow's debut album in 1993. Blending roots, Americana, emotional confessions, melody and intelligence, the album was slow to catch fire. In the UK, Danny Baker was an early champion, playing the singles Run, Baby Run and Leaving Las Vegas on BBC Radio 1 - but it was the slice-of-life bar room anthem All I Wanna Do that turned the album's fortunes around, much to Crow's surprise.

This survey provides an in-depth analysis of knowledge conflicts for large language models (LLMs), highlighting the complex challenges they encounter when blending contextual and parametric knowledge. Our focus is on three categories of knowledge conflicts: context-memory, inter-context, and intra-memory conflict. These conflicts can significantly impact the trustworthiness and performance of LLMs, especially in real-world applications where noise and misinformation are common. By categorizing these conflicts, exploring the causes, examining the behaviors of LLMs under such conflicts, and reviewing available solutions, this survey aims to shed light on strategies for improving the robustness of LLMs, thereby serving as a valuable resource for advancing research in this evolving area.

Existing cross-document event coreference resolution models, which either compute mention similarity directly or enhance mention representation by extracting event arguments (such as location, time, agent, and patient), lacking the ability to utilize document-level information. As a result, they struggle to capture long-distance dependencies. This shortcoming leads to their underwhelming performance in determining coreference for the events where their argument information relies on long-distance dependencies. In light of these limitations, we propose the construction of document-level Rhetorical Structure Theory (RST) trees and cross-document Lexical Chains to model the structural and semantic information of documents. Subsequently, cross-document heterogeneous graphs are constructed and GAT is utilized to learn the representations of events. Finally, a pair scorer calculates the similarity between each pair of events and co-referred events can be recognized using standard clustering algorithm. Additionally, as the existing cross-document event coreference datasets are limited to English, we have developed a large-scale Chinese cross-document event coreference dataset to fill this gap, which comprises 53,066 event mentions and 4,476 clusters. After applying our model on the English and Chinese datasets respectively, it outperforms all baselines by large margins.

The detection of small objects in aerial images is a fundamental task in the field of computer vision. Moving objects in aerial photography have problems such as different shapes and sizes, dense overlap, occlusion by the background, and object blur, however, the original YOLO algorithm has low overall detection accuracy due to its weak ability to perceive targets of different scales. In order to improve the detection accuracy of densely overlapping small targets and fuzzy targets, this paper proposes a dynamic-attention scale-sequence fusion algorithm (DASSF) for small target detection in aerial images. First, we propose a dynamic scale sequence feature fusion (DSSFF) module that improves the up-sampling mechanism and reduces computational load. Secondly, a x-small object detection head is specially added to enhance the detection capability of small targets. Finally, in order to improve the expressive ability of targets of different types and sizes, we use the dynamic head (DyHead). The model we proposed solves the problem of small target detection in aerial images and can be applied to multiple different versions of the YOLO algorithm, which is universal. Experimental results show that when the DASSF method is applied to YOLOv8, compared to YOLOv8n, on the VisDrone-2019 and DIOR datasets, the model shows an increase of 9.2% and 2.4% in the mean average precision (mAP), respectively, and outperforms the current mainstream methods.

Benchmark plays a pivotal role in assessing the advancements of large language models (LLMs). While numerous benchmarks have been proposed to evaluate LLMs' capabilities, there is a notable absence of a dedicated benchmark for assessing their musical abilities. To address this gap, we present ZIQI-Eval, a comprehensive and large-scale music benchmark specifically designed to evaluate the music-related capabilities of LLMs. ZIQI-Eval encompasses a wide range of questions, covering 10 major categories and 56 subcategories, resulting in over 14,000 meticulously curated data entries. By leveraging ZIQI-Eval, we conduct a comprehensive evaluation over 16 LLMs to evaluate and analyze LLMs' performance in the domain of music. Results indicate that all LLMs perform poorly on the ZIQI-Eval benchmark, suggesting significant room for improvement in their musical capabilities. With ZIQI-Eval, we aim to provide a standardized and robust evaluation framework that facilitates a comprehensive assessment of LLMs' music-related abilities. The dataset is available at GitHub\footnote{https://github.com/zcli-charlie/ZIQI-Eval} and HuggingFace\footnote{https://huggingface.co/datasets/MYTH-Lab/ZIQI-Eval}.

While existing alignment paradigms have been integral in developing large language models (LLMs), LLMs often learn an averaged human preference and struggle to model diverse preferences across cultures, demographics, and communities. We propose Modular Pluralism, a modular framework based on multi-LLM collaboration for pluralistic alignment: it "plugs into" a base LLM a pool of smaller but specialized community LMs, where models collaborate in distinct modes to flexibility support three modes of pluralism: Overton, steerable, and distributional. Modular Pluralism is uniquely compatible with black-box LLMs and offers the modular control of adding new community LMs for previously underrepresented communities. We evaluate Modular Pluralism with six tasks and four datasets featuring questions/instructions with value-laden and perspective-informed responses. Extensive experiments demonstrate that Modular Pluralism advances the three pluralism objectives across six black-box and open-source LLMs. Further analysis reveals that LLMs are generally faithful to the inputs from smaller community LLMs, allowing seamless patching by adding a new community LM to better cover previously underrepresented communities.

Tool learning empowers large language models (LLMs) as agents to use external tools and extend their utility. Existing methods employ one single LLM-based agent to iteratively select and execute tools, thereafter incorporating execution results into the next action prediction. Despite their progress, these methods suffer from performance degradation when addressing practical tasks due to: (1) the pre-defined pipeline with restricted flexibility to calibrate incorrect actions, and (2) the struggle to adapt a general LLM-based agent to perform a variety of specialized actions. To mitigate these problems, we propose ConAgents, a Cooperative and interactive Agents framework, which coordinates three specialized agents for tool selection, tool execution, and action calibration separately. ConAgents introduces two communication protocols to enable the flexible cooperation of agents. To effectively generalize the ConAgents into open-source models, we also propose specialized action distillation, enhancing their ability to perform specialized actions in our framework. Our extensive experiments on three datasets show that the LLMs, when equipped with the ConAgents, outperform baselines with substantial improvement (i.e., up to 14% higher success rate).

Just a few weeks after Apple laid out its grand new AI project, it seems Amazon is getting in on the act too. The tech giant is about to fit its smart assistant Alexa with powerful generative AI capabilities that make it much smarter, according to a report. Alexa will be fitted with a'conversational generative AI', it says – although it's unclear what AI model this will actually be. It means she will be able to respond faster and in more human-like language in response to complicated prompts or queries. Amazon's big rivals in tech already have their own AI chatbots, including Google (Gemini) and X (Grok) while Microsoft and Apple have integrations with ChatGPT.

In R. C. Sherriff's novel The Hopkins Manuscript, readers are transported to a world 800 years after a cataclysmic event ended Western civilization. In pursuit of clues about a blank spot in their planet's history, scientists belonging to a new world order discover diary entries in a swamp-infested wasteland formerly known as England. For the inhabitants of this new empire, it is only through this record of a retired school teacher's humdrum rural life, his petty vanities and attempts to breed prize-winning chickens, that they begin to learn about 20th-century Britain. If I were to teach futuristic beings about life on earth, I once believed I could produce a time capsule more profound than Sherriff's small-minded protagonist, Edgar Hopkins. But scrolling through my decade-old Facebook posts this week, I was presented with the possibility that my legacy may be even more drab.

It's been a quiet week of news, but we've been feverishly testing Microsoft's new Surface Pro and Surface Laptop Copilot AI PCs. In this episode, Devindra and Sam will answer your questions about Microsoft's new hardware, and we'll deliver some of our first impressions. It turns out Microsoft may have finally gotten Windows on Arm support right! And some of the Copilot AI features are actually useful, surprisingly enough. But we'll have to wait a few months to test out the controversial Recall feature, which was pulled from the Copilot launch. Listen below or subscribe on your podcast app of choice. If you've got suggestions or topics you'd like covered on the show, be sure to email us or drop a note in the comments! And be sure to check out our other podcast, Engadget News! Wired report: AI search engine Perplexity is ignoring robots.txt Listener question: What do you do with 8 gig fiber home internet? Joining me today is Senior Writer from Engadget, Sam Rutherford. I'm doing okay because we finally have some Copilot Plus PCs. Sam has the Surface Laptop, I have the Surface Pro. And we've just started testing these things. They came in late and we're just like trying to get Arubia as quickly as we can for both of us, but we've got some impressions here. We're going to be taking some questions from our live stream. Cause it's a pretty light news week, but yeah, if you join us Thursday mornings, around 10 30 AM Eastern on our YouTube channel. You too can participate and ask us questions. See us show off some gadgets. We'll show off some stuff live from the Surface Pro. So if you're listening to this in audio form, go back and watch the video, cause you can actually see us test out some features and show off the hardware too. As always folks, if you're enjoying this podcast, please subscribe to us in iTunes or your podcatcher of choice, leave us a review in iTunes. That's always super helpful and drop us [00:01:00]an email at podcast at engadget. Question for you, Sam, what was your first impression upon tearing open the Surface Laptop? Sam: Right away I think it's good they didn't mess with the design. The design was never the issue for the Surfaces, they're, very beautifully crafted. And, opening up and this is going to sound like silly, but it's it functioned exactly like a windows 11 laptop is supposed to. And that was like, Hey, this is actually an improvement from, previous attempts at windows on arm right away. It seems like they, Microsoft has nailed all the important aspects.