Unsupervised Text Style Transfer (UTST) has emerged as a critical task within the domain of Natural Language Processing (NLP), aiming to transfer one stylistic aspect of a sentence into another style without changing its semantics, syntax, or other attributes. This task is especially challenging given the intrinsic lack of parallel text pairings. Among existing methods for UTST tasks, attention masking approach and Large Language Models (LLMs) are deemed as two pioneering methods. However, they have shortcomings in generating unsmooth sentences and changing the original contents, respectively. In this paper, we investigate if we can combine these two methods effectively. We propose four ways of interactions, that are pipeline framework with tuned orders; knowledge distillation from LLMs to attention masking model; in-context learning with constructed parallel examples. We empirically show these multi-way interactions can improve the baselines in certain perspective of style strength, content preservation and text fluency. Experiments also demonstrate that simply conducting prompting followed by attention masking-based revision can consistently surpass the other systems, including supervised text style transfer systems. On Yelp-clean and Amazon-clean datasets, it improves the previously best mean metric by 0.5 and 3.0 absolute percentages respectively, and achieves new SOTA results.

As transformer-based language models are trained on increasingly large datasets and with vast numbers of parameters, finding more efficient alternatives to the standard Transformer has become very valuable. While many efficient Transformers and Transformer alternatives have been proposed, none provide theoretical guarantees that they are a suitable replacement for the standard Transformer. This makes it challenging to identify when to use a specific model and what directions to prioritize for further investigation. In this paper, we aim to understand the capabilities and limitations of efficient Transformers, specifically the Sparse Transformer and the Linear Transformer. We focus on their reasoning capability as exhibited by Chain-of-Thought (CoT) prompts and follow previous works to model them as Dynamic Programming (DP) problems. Our results show that while these models are expressive enough to solve general DP tasks, contrary to expectations, they require a model size that scales with the problem size. Nonetheless, we identify a class of DP problems for which these models can be more efficient than the standard Transformer. We confirm our theoretical results through experiments on representative DP tasks, adding to the understanding of efficient Transformers' practical strengths and weaknesses.

Researchers say LLMs still have a ways to go before they can be used reliably for high-stakes purposes. Shannon Gallagher, a Carnegie Mellon researcher speaking at the conference, said her team was asked last year by the Office of the Director of National Intelligence to explore how LLMs can be used by intelligence agencies. Gallagher said that in her team's study, they devised a "balloon test," in which they prompted LLMs to describe what happened in the high-altitude Chinese surveillance balloon incident last year, as a proxy for the kinds of geopolitical events an intelligence agency might be interested in. The responses ran the gamut, with some of them biased and unhelpful.

With AI services like Microsoft Copilot and Google Bard claiming to be able to easily summarize PDF documents at the drop of a hat, it's a little surprising that Adobe itself hasn't supplied AI tools itself. Adobe on Tuesday announced AI Assistant, a free, beta conversational engine inside of Adobe Reader and Acrobat. Instead of just summarizing the document, the tool will allow users to ask questions of it, clarifying details. Acrobat Standard, Pro, and Teams customers and Acrobat Pro trial users can use the AI Assistant beta today, with features coming to Reader over the coming days and weeks, Adobe said in a statement. Adobe Acrobat is just one of the PDF editors PCWorld recommends.

Acer unveiled a pair of AMD Ryzen 8040 series laptops on Tuesday. Unsurprisingly, given their chips' dedicated neural processing units (NPU), the company is marketing the 2024 Acer Swift Edge 16 and Swift Go 14 as AI workhorses. The Windows 11 machines support OLED displays, Radeon 780M graphics and 32GB of RAM. The Ryzen 8040 chip series, revealed in December, has a dedicated AI Engine that AMD claims makes it up to 1.4 times faster than its predecessors in Llama 2 and AI vision model performance. Acer says the Swift Edge 16 and Swift Go 14 will deploy the NPU for AI-related tasks like PurifiedVoice (remove background noise in calls and recordings) and PurifiedView (blurring backgrounds in images and correcting your eyes' positioning on video calls).

Artificial intelligence could be used to present precisely targeted messages to voters at scale, boosting the power of online political campaigns. How does ChatGPT work and do AI-powered chatbots "think" like us? Microtargeting – the use of online data to tailor messaging to individuals – has been used to powerful effect in past elections and became the focus of a 2018 scandal when journalists exposed the work of political consultancy firm Cambridge Analytica in the US and other countries.

Writing formulas on spreadsheets, such as Microsoft Excel and Google Sheets, is a widespread practice among users performing data analysis. However, crafting formulas on spreadsheets remains a tedious and error-prone task for many end-users, particularly when dealing with complex operations. To alleviate the burden associated with writing spreadsheet formulas, this paper introduces a novel benchmark task called NL2Formula, with the aim to generate executable formulas that are grounded on a spreadsheet table, given a Natural Language (NL) query as input. To accomplish this, we construct a comprehensive dataset consisting of 70,799 paired NL queries and corresponding spreadsheet formulas, covering 21,670 tables and 37 types of formula functions. We realize the NL2Formula task by providing a sequence-to-sequence baseline implementation called fCoder. Experimental results validate the effectiveness of fCoder, demonstrating its superior performance compared to the baseline models. Furthermore, we also compare fCoder with an initial GPT-3.5 model (i.e., text-davinci-003). Lastly, through in-depth error analysis, we identify potential challenges in the NL2Formula task and advocate for further investigation.

While learning with limited labelled data can improve performance when the labels are lacking, it is also sensitive to the effects of uncontrolled randomness introduced by so-called randomness factors (e.g., varying order of data). We propose a method to systematically investigate the effects of randomness factors while taking the interactions between them into consideration. To measure the true effects of an individual randomness factor, our method mitigates the effects of other factors and observes how the performance varies across multiple runs. Applying our method to multiple randomness factors across in-context learning and fine-tuning approaches on 7 representative text classification tasks and meta-learning on 3 tasks, we show that: 1) disregarding interactions between randomness factors in existing works caused inconsistent findings due to incorrect attribution of the effects of randomness factors, such as disproving the consistent sensitivity of in-context learning to sample order even with random sample selection; and 2) besides mutual interactions, the effects of randomness factors, especially sample order, are also dependent on more systematic choices unexplored in existing works, such as number of classes, samples per class or choice of prompt format.

Large language models (LLMs) have demonstrated impressive abilities in generating unstructured natural language according to instructions. However, their performance can be inconsistent when tasked with producing text that adheres to specific structured formats, which is crucial in applications like named entity recognition (NER) or relation extraction (RE). To address this issue, this paper introduces an efficient method, G&O, to enhance their structured text generation capabilities. It breaks the generation into a two-step pipeline: initially, LLMs generate answers in natural language as intermediate responses. Subsequently, LLMs are asked to organize the output into the desired structure, using the intermediate responses as context. G&O effectively separates the generation of content from the structuring process, reducing the pressure of completing two orthogonal tasks simultaneously. Tested on zero-shot NER and RE, the results indicate a significant improvement in LLM performance with minimal additional efforts. This straightforward and adaptable prompting technique can also be combined with other strategies, like self-consistency, to further elevate LLM capabilities in various structured text generation tasks.

The advent of Large Language Models (LLMs) has marked significant achievements in language processing and reasoning capabilities. Despite their advancements, LLMs face vulnerabilities to data poisoning attacks, where adversaries insert backdoor triggers into training data to manipulate outputs for malicious purposes. This work further identifies additional security risks in LLMs by designing a new data poisoning attack tailored to exploit the instruction tuning process. We propose a novel gradient-guided backdoor trigger learning approach to identify adversarial triggers efficiently, ensuring an evasion of detection by conventional defenses while maintaining content integrity. Through experimental validation across various LLMs and tasks, our strategy demonstrates a high success rate in compromising model outputs; poisoning only 1\% of 4,000 instruction tuning samples leads to a Performance Drop Rate (PDR) of around 80\%. Our work highlights the need for stronger defenses against data poisoning attack, offering insights into safeguarding LLMs against these more sophisticated attacks. The source code can be found on this GitHub repository: https://github.com/RookieZxy/GBTL/blob/main/README.md.