The previous work on controllable text generation is organized using a new schema we provide in this study. Seven components make up the schema, and each one is crucial to the creation process. To accomplish controlled generation for scientific literature, we describe the various modulation strategies utilised to modulate each of the seven components. We also offer a theoretical study and qualitative examination of these methods. This insight makes possible new architectures based on combinations of these components. Future research will compare these methods empirically to learn more about their strengths and utility.

Large language models that exhibit instruction-following behaviour represent one of the biggest recent upheavals in conversational interfaces, a trend in large part fuelled by the release of OpenAI's ChatGPT, a proprietary large language model for text generation fine-tuned through reinforcement learning from human feedback (LLM+RLHF). We review the risks of relying on proprietary software and survey the first crop of open-source projects of comparable architecture and functionality. The main contribution of this paper is to show that openness is differentiated, and to offer scientific documentation of degrees of openness in this fast-moving field. We evaluate projects in terms of openness of code, training data, model weights, RLHF data, licensing, scientific documentation, and access methods. We find that while there is a fast-growing list of projects billing themselves as 'open source', many inherit undocumented data of dubious legality, few share the all-important instruction-tuning (a key site where human annotation labour is involved), and careful scientific documentation is exceedingly rare. Degrees of openness are relevant to fairness and accountability at all points, from data collection and curation to model architecture, and from training and fine-tuning to release and deployment.

Instruction fine-tuning has recently emerged as a promising approach for improving the zero-shot capabilities of Large Language Models (LLMs) on new tasks. This technique has shown particular strength in improving the performance of modestly sized LLMs, sometimes inducing performance competitive with much larger model variants. In this paper we ask two questions: (1) How sensitive are instruction-tuned models to the particular phrasings of instructions, and, (2) How can we make them more robust to such natural language variation? To answer the former, we collect a set of 319 instructions manually written by NLP practitioners for over 80 unique tasks included in widely used benchmarks, and we evaluate the variance and average performance of these instructions as compared to instruction phrasings observed during instruction fine-tuning. We find that using novel (unobserved) but appropriate instruction phrasings consistently degrades model performance, sometimes substantially so. Further, such natural instructions yield a wide variance in downstream performance, despite their semantic equivalence. Put another way, instruction-tuned models are not especially robust to instruction re-phrasings. We propose a simple method to mitigate this issue by introducing ``soft prompt'' embedding parameters and optimizing these to maximize the similarity between representations of semantically equivalent instructions. We show that this method consistently improves the robustness of instruction-tuned models.

In open-domain question answering, due to the ambiguity of questions, multiple plausible answers may exist. To provide feasible answers to an ambiguous question, one approach is to directly predict all valid answers, but this can struggle with balancing relevance and diversity. An alternative is to gather candidate answers and aggregate them, but this method can be computationally costly and may neglect dependencies among answers. In this paper, we present AmbigPrompt to address the imperfections of existing approaches to answering ambiguous questions. Specifically, we integrate an answering model with a prompting model in an iterative manner. The prompting model adaptively tracks the reading process and progressively triggers the answering model to compose distinct and relevant answers. Additionally, we develop a task-specific post-pretraining approach for both the answering model and the prompting model, which greatly improves the performance of our framework. Empirical studies on two commonly-used open benchmarks show that AmbigPrompt achieves state-of-the-art or competitive results while using less memory and having a lower inference latency than competing approaches. Additionally, AmbigPrompt also performs well in low-resource settings. The code are available at: https://github.com/sunnweiwei/AmbigPrompt.

Large language models (LLMs) have significantly advanced the field of natural language processing, with GPT models at the forefront. While their remarkable performance spans a range of tasks, adapting LLMs for real-world business scenarios still poses challenges warranting further investigation. This paper presents an empirical analysis aimed at bridging the gap in adapting LLMs to practical use cases. To do that, we select the question answering (QA) task of insurance as a case study due to its challenge of reasoning. Based on the task we design a new model relied on LLMs which are empowered by additional knowledge extracted from insurance policy rulebooks and DBpedia. The additional knowledge helps LLMs to understand new concepts of insurance for domain adaptation. Preliminary results on two QA datasets show that knowledge enhancement significantly improves the reasoning ability of GPT-3.5 (55.80% and 57.83% in terms of accuracy). The analysis also indicates that existing public knowledge bases, e.g., DBPedia is beneficial for knowledge enhancement. Our findings reveal that the inherent complexity of business scenarios often necessitates the incorporation of domain-specific knowledge and external resources for effective problem-solving.

American Accountability Foundation spokesman Robert Donachie says the left is trying to use AI to'push their agenda on the American people.' A co-founder of artificial intelligence leader OpenAI is warning that superintelligence must be controlled in order to prevent the extinction of the human race. "Superintelligence will be the most impactful technology humanity has ever invented, and could help us solve many of the world's most important problems. But the vast power of superintelligence could also be very dangerous, and could lead to the disempowerment of humanity or even human extinction," Ilya Sutskever and head of alignment Jan Leike wrote in a Tuesday blog post, saying they believe such advancements could arrive as soon as this decade. They said managing such risks would require new institutions for governance and solving the problem of superintelligence alignment: ensuring AI systems much smarter than humans "follow human intent."

We owe our understanding of human behavior thanks, in part, to Bob. He spends hours some days as a subject in academic psychology studies, filling out surveys on crowd-work platforms like Amazon's Mechanical Turk, where users perform simple digital tasks for small sums of money. The questionnaires often prompt him to recall a time he felt sad, or isolated, or something likewise morose. Sometimes typing his sob stories over and over gets "really really monotonous," he says. So Bob asks ChatGPT to pour its simulacrum of a heart out instead.

From a Furby to the'world's most advanced' humanoid robot, ChatGPT has already been hooked up to a range of bizarre things. But the latest use for the AI chatbot is arguably the strangest yet, as Lovense has announced that it has integrated ChatGPT in a sex toy, dubbed the ChatGPT Pleasure Companion. Users can divulge their sexual fantasies to the sex toy, before it uses ChatGPT to write a story. 'The higher the intensity of the story, the stronger and faster the toy's reaction will be,' Lovense explained. The system is currently in beta, and has been described as a way to'explore your sexuality and boundaries completely independently.'

Then each researcher wrote an additional text in Bosnian, Czech, German, Latvian, Slovak, Spanish, or Swedish. Those texts were passed through either the AI translation tool DeepL or Google Translate to translate them into English. The team then used ChatGPT to generate two additional texts each, which they slightly tweaked in an effort to hide that it'd been AI-generated. One set was edited manually by the researchers, who reordered sentences and exchanged words, while another was rewritten using an AI paraphrasing tool called Quillbot. In the end, they had 54 documents to test the detection tools on.

ChatGPT rocketed to an estimated 100 million monthly users in its first two months, according to a report by analysts from the Swiss bank UBS. The bot's ability to have complex conversations, write poetry and pass professional exams impressed regular users and AI experts alike. Tech pundits called it the fastest-growing consumer app in history, and its rapid growth set off an arms race among Big Tech giants to push out their competing products.