19 citations · 39 across the 8 of their papers we have counts for
8 papers
Content Moderation in TV Search: Balancing Policy Compliance, Relevance, and User Experience
Adeep Hande, Kishorekumar Sundararajan, Sardar Hamidian +1
Millions of people rely on search functionality to find and explore content on entertainment platforms. Modern search systems use a combination of candidate generation and ranking…
Non-Determinism of "Deterministic" LLM Settings
Berk Atil, Sarp Aykent, Alexa Chittams +10
LLM (large language model) practitioners commonly notice that outputs can vary for the same inputs under settings expected to be deterministic. Yet the questions of how pervasive t…
Words Worth a Thousand Pictures: Measuring and Understanding Perceptual Variability in Text-to-Image Generation
Raphael Tang, Xinyu Zhang, Lixinyu Xu +5
Diffusion models are the state of the art in text-to-image generation, but their perceptual variability remains understudied. In this paper, we examine how prompts affect image var…
"Ask Me Anything": How Comcast Uses LLMs to Assist Agents in Real Time
Scott Rome, Tianwen Chen, Raphael Tang +2
Customer service is how companies interface with their customers. It can contribute heavily towards the overall customer satisfaction. However, high-quality service can become expe…
What Do Llamas Really Think? Revealing Preference Biases in Language Model Representations
Raphael Tang, Xinyu Zhang, Jimmy Lin +1
Do large language models (LLMs) exhibit sociodemographic biases, even when they decline to respond? To bypass their refusal to "speak," we study this research question by probing c…
Found in the Middle: Permutation Self-Consistency Improves Listwise Ranking in Large Language Models
Raphael Tang, Xinyu Zhang, Xueguang Ma +2
Large language models (LLMs) exhibit positional bias in how they use context, which especially complicates listwise ranking. To address this, we propose permutation self-consistenc…
SpeechNet: Weakly Supervised, End-to-End Speech Recognition at Industrial Scale
Raphael Tang, Karun Kumar, Gefei Yang +7
End-to-end automatic speech recognition systems represent the state of the art, but they rely on thousands of hours of manually annotated speech for training, as well as heavyweigh…
What the DAAM: Interpreting Stable Diffusion Using Cross Attention
Raphael Tang, Linqing Liu, Akshat Pandey +6
Large-scale diffusion neural networks represent a substantial milestone in text-to-image generation, but they remain poorly understood, lacking interpretability analyses. In this p…