A key challenge in aligning TTA models lies in the difficulty of creating preference pairs, as TTA lacks structured mechanisms like verifiable rewards or gold-standard answers available for Large Language Models (LLMs). We demonstrate that the audio preference dataset generated using CRPO outperforms existing alternatives. We open source all code and models to support further research in TTA generation. Audio plays a vital role in daily life and creative industries, from enhancing communication and storytelling to enriching experiences in music, sound effects, and podcasts. Recent advancements in text-to-audio (TTA) generation (Majumder et al., 2024; Ghosal et al., 2023; Liu et al., 2023; 2024b; Xue et al., 2024; Vyas et al., 2023; Huang et al., 2023b;a) and offer a transformative approach, enabling the automatic creation of diverse and expressive audio content directly from textual descriptions. This technology holds immense potential to streamline audio production workflows and unlock new possibilities in multimedia content creation. However, many existing models face challenges with controllability, occasionally struggling to fully capture the details in the input prompts, especially when the prompts are complex. This can sometimes result in generated audio that omits certain events or diverges from the user intent. At times, the generated audio may even contain input-adjacent, but unmentioned and unintended, events, that could be characterized as hallucinations. In contrast, the recent advancements in Large Language Models (LLMs) (Ouyang et al., 2022) have been significantly driven by the alignment stage after pre-training and supervised fine-tuning. This alignment stage, often leveraging reinforcement learning from human feedback (RLHF) or other reward-based optimization methods, endows the generated outputs with human preferences, ethical considerations, and task-specific requirements (Ouyang et al., 2022).

Generative multimodal content is increasingly prevalent in much of the content creation arena, as it has the potential to allow artists and media personnel to create pre-production mockups by quickly bringing their ideas to life. The generation of audio from text prompts is an important aspect of such processes in the music and film industry. Many of the recent diffusion-based text-to-audio models focus on training increasingly sophisticated diffusion models on a large set of datasets of prompt-audio pairs. These models do not explicitly focus on the presence of concepts or events and their temporal ordering in the output audio with respect to the input prompt. Our hypothesis is focusing on how these aspects of audio generation could improve audio generation performance in the presence of limited data. As such, in this work, using an existing text-to-audio model Tango, we synthetically create a preference dataset where each prompt has a winner audio output and some loser audio outputs for the diffusion model to learn from. The loser outputs, in theory, have some concepts from the prompt missing or in an incorrect order. We fine-tune the publicly available Tango text-to-audio model using diffusion-DPO (direct preference optimization) loss on our preference dataset and show that it leads to improved audio output over Tango and AudioLDM2, in terms of both automatic- and manual-evaluation metrics.

The widespread applicability and increasing omnipresence of LLMs have instigated a need to align LLM responses to user and stakeholder preferences. Many preference optimization approaches have been proposed that fine-tune LLM parameters to achieve good alignment. However, such parameter tuning is known to interfere with model performance on many tasks. Moreover, keeping up with shifting user preferences is tricky in such a situation. Decoding-time alignment with reward model guidance solves these issues at the cost of increased inference time. However, most of such methods fail to strike the right balance between exploration and exploitation of reward -- often due to the conflated formulation of these two aspects - to give well-aligned responses. To remedy this we decouple these two aspects and implement them in an evolutionary fashion: exploration is enforced by decoding from mutated instructions and exploitation is represented as the periodic replacement of poorly-rewarded generations with well-rewarded ones. Empirical evidences indicate that this strategy outperforms many preference optimization and decode-time alignment approaches on two widely accepted alignment benchmarks AlpacaEval 2 and MT-Bench. Our implementation will be available at: https://darwin-alignment.github.io.

Authorship verification (AV) is a fundamental task in natural language processing (NLP) and computational linguistics, with applications in forensic analysis, plagiarism detection, and identification of deceptive content. Existing AV techniques, including traditional stylometric and deep learning approaches, face limitations in terms of data requirements and lack of explainability. To address these limitations, this paper proposes PromptAV, a novel technique that leverages Large-Language Models (LLMs) for AV by providing step-by-step stylometric explanation prompts. PromptAV outperforms state-of-the-art baselines, operates effectively with limited training data, and enhances interpretability through intuitive explanations, showcasing its potential as an effective and interpretable solution for the AV task.