27 citations · 38 across the 10 of their papers we have counts for
12 papers
Debate Training Reduces Reward Hacking in RLAIF
Zachary Kenton, Lili Janzer, Rory Greig +8
We demonstrate that RL finetuning an LLM using debate, a two-player adversarial game between a generator and a critic adjudicated by a weaker LLM judge, reduces reward hacking comp…
Human-AI Complementarity: A Goal for Amplified Oversight
Rishub Jain, Sophie Bridgers, Lili Janzer +3
Human feedback is critical for aligning AI systems to human values. As AI capabilities improve and AI is used to tackle more challenging tasks, verifying quality and safety becomes…
"Just a strange pic": Evaluating 'safety' in GenAI Image safety annotation tasks from diverse annotators' perspectives
Ding Wang, Mark Díaz, Charvi Rastogi +10
Understanding what constitutes safety in AI-generated content is complex. While developers often rely on predefined taxonomies, real-world safety judgments also involve personal, s…
Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models
Charvi Rastogi, Tian Huey Teh, Pushkar Mishra +10
Current text-to-image (T2I) models often fail to account for diverse human experiences, leading to misaligned systems. We advocate for pluralistic alignment, where an AI understand…
Decoding Safety Feedback from Diverse Raters: A Data-driven Lens on Responsiveness to Severity
Pushkar Mishra, Charvi Rastogi, Stephen R. Pfohl +9
Ensuring the safety of Generative AI requires a nuanced understanding of pluralistic viewpoints. In this paper, we introduce a novel data-driven approach for analyzing ordinal safe…
Insights on Disagreement Patterns in Multimodal Safety Perception across Diverse Rater Groups
Charvi Rastogi, Tian Huey Teh, Pushkar Mishra +10
AI systems crucially rely on human ratings, but these ratings are often aggregated, obscuring the inherent diversity of perspectives in real-world phenomenon. This is particularly…
Ensemble prosody prediction for expressive speech synthesis
Tian Huey Teh, Vivian Hu, Devang S Ram Mohan +7
Generating expressive speech with rich and varied prosody continues to be a challenge for Text-to-Speech. Most efforts have focused on sophisticated neural architectures intended t…
Controllable Prosody Generation With Partial Inputs
Dan Andrei Iliescu, Devang Savita Ram Mohan, Tian Huey Teh +1
We address the problem of human-in-the-loop control for generating prosody in the context of text-to-speech synthesis. Controlling prosody is challenging because existing generativ…
ADEPT: A Dataset for Evaluating Prosody Transfer
Alexandra Torresquintero, Tian Huey Teh, Christopher G. R. Wallis +6
Text-to-speech is now able to achieve near-human naturalness and research focus has shifted to increasing expressivity. One popular method is to transfer the prosody from a referen…
Ctrl-P: Temporal Control of Prosodic Variation for Speech Synthesis
Devang S Ram Mohan, Vivian Hu, Tian Huey Teh +6
Text does not fully specify the spoken form, so text-to-speech models must be able to learn from speech data that vary in ways not explained by the corresponding text. One way to r…
Incremental Text to Speech for Neural Sequence-to-Sequence Models using Reinforcement Learning
Devang S Ram Mohan, Raphael Lenain, Lorenzo Foglianti +4
Modern approaches to text to speech require the entire input character sequence to be processed before any audio is synthesised. This latency limits the suitability of such models…
Phonological Features for 0-shot Multilingual Speech Synthesis
Marlene Staib, Tian Huey Teh, Alexandra Torresquintero +4
Code-switching---the intra-utterance use of multiple languages---is prevalent across the world. Within text-to-speech (TTS), multilingual models have been found to enable code-swit…