Although LLMs have been widely adopted for creative content generation, a single-pass process often struggles to produce high-quality long narratives. How to effectively revise and improve long narrative scripts like scriptwriters remains a significant challenge, as it demands a comprehensive understanding of the entire context to identify global structural issues and local detailed flaws, as well as coordinating revisions at multiple granularities and locations. Direct modifications by LLMs typically introduce inconsistencies between local edits and the overall narrative requirements. To address these issues, we propose Dramaturge, a task and feature oriented divide-and-conquer approach powered by hierarchical multiple LLM agents. It consists of a Global Review stage to grasp the overall storyline and structural issues, a Scene-level Review stage to pinpoint detailed scene and sentence flaws, and a Hierarchical Coordinated Revision stage that coordinates and integrates structural and detailed improvements throughout the script. The top-down task flow ensures that high-level strategies guide local modifications, maintaining contextual consistency. The review and revision workflow follows a coarse-to-fine iterative process, continuing through multiple rounds until no further substantive improvements can be made. Comprehensive experiments show that Dra-maturge significantly outperforms all baselines in terms of script-level overall quality and scene-level details. Our approach is plug-and-play and can be easily integrated into existing methods to improve the generated scripts.

Methods designed to remove such knowledge must do so from all prompt directions, in a multi-lingual capacity and without degrading general model performance. To this end, we introduce the targeted angular reversal (TARS) method of knowledge removal from LLMs. The TARS method firstly leverages the LLM in combination with a detailed prompt to aggregate information about a selected concept in the internal representation space of the LLM. It then refines this approximate concept vector to trigger the concept token with high probability, by perturbing the approximate concept vector with noise and transforming it into token scores with the language model head. The feed-forward weight vectors in the LLM which operate directly on the internal representation space, and have the highest cosine similarity with this refined targeting vector, are then replaced by a reversed targeting vector, thus limiting the ability of the concept to propagate through the model. The modularity of the TARS method allows for a sequential removal of concepts from Llama 3.1 8B, such as the famous literary detective Sherlock Holmes, and the planet Saturn. It is demonstrated that the probability of triggering target concepts can be reduced to 0.00 with as few as 1 TARS edit, whilst simultaneously removing the knowledge bi-directionally. Moreover, knowledge is shown to be removed across all languages despite only being targeted in English. Importantly, TARS has minimal impact on the general model capabilities, as after removing 5 diverse concepts in a modular fashion, there is minimal KL divergence in the next token probabilities of the LLM on large corpora of Wikipedia text (median of 0.002).

The recent success of large language models (LLMs) has attracted widespread interest to develop role-playing conversational agents personalized to the characteristics and styles of different speakers to enhance their abilities to perform both general and special purpose dialogue tasks. However, the ability to personalize the generated utterances to speakers, whether conducted by human or LLM, has not been well studied. To bridge this gap, our study introduces a novel evaluation challenge: speaker verification in agent-generated conversations, which aimed to verify whether two sets of utterances originate from the same speaker. To this end, we assemble a large dataset collection encompassing thousands of speakers and their utterances. We also develop and evaluate speaker verification models under experiment setups. We further utilize the speaker verification models to evaluate the personalization abilities of LLM-based role-playing models. Comprehensive experiments suggest that the current role-playing models fail in accurately mimicking speakers, primarily due to their inherent linguistic characteristics.

Large language models (LLMs) can be used to serve as agents to simulate human behaviors, given the powerful ability to understand human instructions and provide high-quality generated texts. Such ability stimulates us to wonder whether LLMs can simulate a person in a higher form than simple human behaviors. Therefore, we aim to train an agent with the profile, experience, and emotional states of a specific person instead of using limited prompts to instruct ChatGPT API. In this work, we introduce Character-LLM that teach LLMs to act as specific people such as Beethoven, Queen Cleopatra, Julius Caesar, etc. Our method focuses on editing profiles as experiences of a certain character and training models to be personal simulacra with these experiences. To assess the effectiveness of our approach, we build a test playground that interviews trained agents and evaluates whether the agents \textit{memorize} their characters and experiences. Experimental results show interesting observations that help build future simulacra of humankind.

It's hard to imagine one Death Eater kissing another one on the cheek while more of Voldemort's supporters gather around them and applaud -- unless you're a computer. Botnik Studios, a company that uses algorithms to train computers to behave in certain ways, has produced a brand new chapter from a new Harry Potter book. While a computer can certainly mimic elements of the famous series, though, it can hardly capture J.K. Rowling's magic -- which is why the result, called Harry Potter and the Portrait of What Looked Like a Large Pile of Ash, is so strange and funny that fans are begging for more. Too funny: Botnik Studios used predictive keyboards to write a new chapter in a new Harry Potter book. It's called Harry Potter and the Portrait of What Looked Like a Large Pile of Ash Has that computer been Confunded?

It's hard to come up with ideas for Halloween costumes, especially when it seems like all the good ones are taken. And don't you hate showing up at a party only to discover that there's *another* pajama cardinalfish? I train neural networks, a type of machine learning algorithm, to write humor by giving them datasets that they have to teach themselves to mimic. They can sometimes do a surprisingly good job, coming up with a metal band called Chaosrug, a craft beer called Yamquak and another called The Fine Stranger (which now exists!), and a My Little Pony called Blue Cuss. So, I wanted to find out if a neural network could help invent Halloween costumes.

Microsoft reveals'what if' face mashup system that can you show everything from Voldemort in Kiss to Donald Trump in Game of Thrones The chatbot has since taken the internet by storm, with users creating'what if' images for every imaginable situation. This includes'What if Trump is Cersei Lannister' proposed by Twitter user Jeremy Randall Pictured is a terrifying baby-Yoda mashup it created when asked'What if Yoda were BB-8?' The bot created an image to visualize'What if Chewbacca were Yoda?' There are often those moments in life that cause us to wonder, 'what if' – but, Microsoft's new chatbot might make you wish you never had. The new bot called'Murphy' generates mashup images for any hypothetical face combination, with hilarious, and often terrifying, results Twitter user Stephen Bell asked the bot, 'What if Voldemort was in Kiss?' Valley Stream Best Buy associates gift a teen with a Wii U'I'm going to wing walk!' Schofield talks to Duke about wing walk Prince Philip reminisces about expansion of Duke of Edinburgh awards Homeowner trolls bungling burglar with Mission Impossible theme'They make each other laugh': Countess Sophie on the Duke and Queen Hunters forced to shoot a wild bear dead as it charges towards them'I wanted the painting!': Joanna Lumley jokes about Duke's artwork Documentary director attacked by gang of immigrants in Stockholm Adorable baby dressed as Lion comes face to face with real one Hammer wielding thugs smash car windows and threaten man Adorable dog won't allow owner to stop scratching his belly Ferrari crashes into pedestrians while racing near Battersea Dogs Home Adorable dog won't allow owner to stop scratching his belly Terminally-ill boy, five, dies in Santa Claus' arms after... Missing North Carolina girl who was last seen aged 15... Trump's Iran stance could threaten a WORLD WAR and the... Woman left with huge bill after Plenty of Fish date eats... Model, 32, claims her MIT-grad hedge-funder boyfriend, 29,... Blood-spattered walls, unbearable odours and houses where... Best Buy employees in Long Island chip in to buy a $300 WiiU... Nothing like retail therapy!

I trained an LSTM Recurrent Neural Network (a deep learning algorithm) on the first four Harry Potter books. I then asked it to produce a chapter based on what it learned. He looked like Madame Maxime. When she strode up the wrong staircase to visit himself. "I'm afraid I've definitely been suspended from power, no chance -- indeed?" said Snape.