Table 7: Extensions of Table 1 with more details of prompts used to generate class-conditioned texts for different GLUE tasks. SST-2 and CoLA are single-sequence classification tasks and the rest are sequence-pair classification tasks. Generation for CoLA does not use prompts but by varying sampling temperatures. Text generation with CTRL [23] requires starting with control codes, and we use the ones that correspond to the pretraining corpus where the first sequence is sampled: For MNLI, RTE and MRPC, the first sequence is sampled from Wikipedia; for QNLI and QQP, the first sequence is sampled from OpenWebText [17]. The prompts used for SST-2 are part of the CTRL [23] codes.

A.1 Detailed Re-alignment Task Formulation and Training Setup In Figure A1, we show the procedure for converting the data samples in the alignment datasets into training data of AEM (negative samples used in AIL are generated similarly). In DP-inferred chain-of-edits (CoEs), we use a few special tokens to mark the editing operations (with their position and content). Then our decipher module will translate these special tokens into natural language. As the final step, we add a special token [SEP] between Context + Source and the ground truth Chain-of-Edits (CoEs) and Target, as a boundary signal similar to the settings in text-to-text training. We also augment the data by using different sets of costs for the editing operations (as discussed in Section 3.2, and footnote 3).

This paper introduces a media service model that exploits artificial intelligence (AI) video generators at the receive end. This proposal deviates from the traditional multimedia ecosystem, completely relying on in-house production, by shifting part of the content creation onto the receiver. We bring a semantic process into the framework, allowing the distribution network to provide service elements that prompt the content generator, rather than distributing encoded data of fully finished programs. The service elements include fine-tailored text descriptions, lightweight image data of some objects, or application programming interfaces, comprehensively referred to as semantic sources, and the user terminal translates the received semantic data into video frames. Empowered by the random nature of generative AI, the users could then experience super-personalized services accordingly. The proposed idea incorporates the situations in which the user receives different service providers' element packages; a sequence of packages over time, or multiple packages at the same time. Given promised in-context coherence and content integrity, the combinatory dynamics will amplify the service diversity, allowing the users to always chance upon new experiences. This work particularly aims at short-form videos and advertisements, which the users would easily feel fatigued by seeing the same frame sequence every time. In those use cases, the content provider's role will be recast as scripting semantic sources, transformed from a thorough producer. Overall, this work explores a new form of media ecosystem facilitated by receiver-embedded generative models, featuring both random content dynamics and enhanced delivery efficiency simultaneously.

Nowadays, the usage of Large Language Models (LLMs) has increased, and LLMs have been used to generate texts in different languages and for different tasks. Additionally, due to the participation of remarkable companies such as Google and OpenAI, LLMs are now more accessible, and people can easily use them. However, an important issue is how we can detect AI-generated texts from human-written ones. In this article, we have investigated the problem of AI-generated text detection from two different aspects: semantics and syntax. Finally, we presented an AI model that can distinguish AI-generated texts from human-written ones with high accuracy on both multilingual and monolingual tasks using the M4 dataset. According to our results, using a semantic approach would be more helpful for detection. However, there is a lot of room for improvement in the syntactic approach, and it would be a good approach for future work.

Ever posted or left a comment on Reddit? Your words will soon be used to train an artificial intelligence companies' models, according to Bloomberg. The website signed a deal that's "worth about 60 million on an annualized basis" earlier this year, it reportedly told potential investors ahead of its expected initial public offering (IPO). Bloomberg didn't name the "large AI company" that's paying Reddit millions for access to its content, but their agreement could apparently serve as a model for future contracts, which could mean more multi-million deals for the firm. Reddit first announced that it was going to start charging companies for API access in April last year.

Rachelle Hampton and Candice Lim catch up on the latest stories churning the Taylor Swift media machine, from her lawyers sending a cease and desist letter to a college student, to her possibly leading a groundbreaking case against AI deepfakes. Then, they break down the backlash surrounding Emily Mariko, who was criticized by her followers for selling out -- and shelling out -- a tote bag. This podcast is produced by Se'era Spragley Ricks, Daisy Rosario, Candice Lim and Rachelle Hampton.

OpenAI has unveiled its latest artificial intelligence system, a program called Sora that can transform text descriptions into photorealistic videos. The video generation model is spurring excitement about advancing AI technology, along with growing concerns over how artificial deepfake videos worsen misinformation and disinformation during a pivotal election year worldwide. The Sora AI model can currently create videos up to 60 seconds long using either text instructions alone or text combined with an image. One demonstration video starts with a text prompt that describes how "a stylish woman walks down a Tokyo street filled with warm glowing neon and animated city signage". Other examples include a dog frolicking in the snow, vehicles driving along roads and more fantastical scenarios such as sharks swimming in midair between city skyscrapers.

Previous in-context learning (ICL) research has focused on tasks such as classification, machine translation, text2table, etc., while studies on whether ICL can improve human-like dialogue generation are scarce. Our work fills this gap by systematically investigating the ICL capabilities of large language models (LLMs) in persona-based dialogue generation, conducting extensive experiments on high-quality real human Chinese dialogue datasets. From experimental results, we draw three conclusions: 1) adjusting prompt instructions is the most direct, effective, and economical way to improve generation quality; 2) randomly retrieving demonstrations (demos) achieves the best results, possibly due to the greater diversity and the amount of effective information; counter-intuitively, retrieving demos with a context identical to the query performs the worst; 3) even when we destroy the multi-turn associations and single-turn semantics in the demos, increasing the number of demos still improves dialogue performance, proving that LLMs can learn from corrupted dialogue demos. Previous explanations of the ICL mechanism, such as $n$-gram induction head, cannot fully account for this phenomenon.

For the past couple of weeks, I've been making a home video on my phone, using Apple's iMovie software. The idea is to weave together clips of my family that I've taken during the month of February; I plan to keep working on it until March. So far, the movie shows my five-month-old daughter cooing and waving her arms; my five-year-old son chasing me with a snowball; and a visit to the spooky, run-down amusement park in our town, among other things. I thought of my movie while absorbing the announcement, yesterday, of Sora, an astonishing new text-to-video system from OpenAI, the makers of ChatGPT. Sora can take prompts from users and produce detailed, inventive, and photorealistic one-minute-long videos.

Yesterday afternoon, OpenAI teased Sora, a video-generation model that promises to convert written text prompts into highly realistic videos. Footage released by the company depicts such examples as "a Shiba Inu dog wearing a beret and black turtleneck" and "in an ornate, historical hall, a massive tidal wave peaks and begins to crash." The excitement from the press has been reminiscent of the buzz surrounding the image creator DALL-E or ChatGPT in 2022: Sora is described as "eye-popping," "world-changing," and "breathtaking, yet terrifying." The imagery is genuinely impressive. At a glance, one example of an animated "fluffy monster" looks better than Shrek; an "extreme close up" of a woman's eye, complete with a reflection of the scene in front of her, is startlingly lifelike.