7 citations · 32 across the 26 of their papers we have counts for
9 papers · 1 filter
Prompt Expansion for Adaptive Text-to-Image Generation
Siddhartha Datta, Alexander Ku, Deepak Ramachandran +1
Text-to-image generation models are powerful but difficult to use. Users craft specific prompts to get better images, though the images can be repetitive. This paper proposes a Pro…
Rich Human Feedback for Text-to-Image Generation
Youwei Liang, Junfeng He, Gang Li +15
Recent Text-to-Image (T2I) generation models such as Stable Diffusion and Imagen have made significant progress in generating high-resolution images based on text descriptions. How…
Helping or Herding? Reward Model Ensembles Mitigate but do not Eliminate Reward Hacking
Jacob Eisenstein, Chirag Nagpal, Alekh Agarwal +9
Reward models play a key role in aligning language model applications towards human preferences. However, this setup creates an incentive for the language model to exploit errors i…
Modeling subjectivity (by Mimicking Annotator Annotation) in toxic comment identification across diverse communities
Senjuti Dutta, Sid Mittal, Sherol Chen +6
The prevalence and impact of toxic discussions online have made content moderation crucial.Automated systems can play a vital role in identifying toxicity, and reducing the relianc…
Demystifying Embedding Spaces using Large Language Models
Guy Tennenholtz, Yinlam Chow, Chih-Wei Hsu +6
Embeddings have become a pivotal means to represent complex, multi-faceted information about entities, concepts, and relationships in a condensed and useful format. Nevertheless, t…
TaskLAMA: Probing the Complex Task Understanding of Language Models
Quan Yuan, Mehran Kazemi, Xin Xu +3
Structured Complex Task Decomposition (SCTD) is the problem of breaking down a complex real-world task (such as planning a wedding) into a directed acyclic graph over individual st…
BoardgameQA: A Dataset for Natural Language Reasoning with Contradictory Information
Mehran Kazemi, Quan Yuan, Deepti Bhatia +4
Automated reasoning with unstructured natural text is a key requirement for many potential applications of NLP and for developing robust AI systems. Recently, Language Models (LMs)…
Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play
Jeremiah Zhe Liu, Krishnamurthy Dj Dvijotham, Jihyeon Lee +4
Standard empirical risk minimization (ERM) training can produce deep neural network (DNN) models that are accurate on average but under-perform in under-represented population subg…
Understanding Finetuning for Factual Knowledge Extraction from Language Models
Mehran Kazemi, Sid Mittal, Deepak Ramachandran
Language models (LMs) pretrained on large corpora of text from the web have been observed to contain large amounts of various types of knowledge about the world. This observation h…