40 citations · 54 across the 5 of their papers we have counts for
7 papers
Impact of Preference Noise on the Alignment Performance of Generative Language Models
Yang Gao, Dana Alon, Donald Metzler
A key requirement in developing Generative Language Models (GLMs) is to have their values aligned with human values. Preference-based alignment is a widely used paradigm for this p…
PaRaDe: Passage Ranking using Demonstrations with Large Language Models
Andrew Drozdov, Honglei Zhuang, Zhuyun Dai +8
Recent studies show that large language models (LLMs) can be instructed to effectively perform zero-shot passage re-ranking, in which the results of a first stage retrieval method,…
Gen-IR @ SIGIR 2023: The First Workshop on Generative Information Retrieval
Gabriel Bénédict, Ruqing Zhang, Donald Metzler
Generative information retrieval (IR) has experienced substantial growth across multiple research communities (e.g., information retrieval, computer vision, natural language proces…
LAIT: Efficient Multi-Segment Encoding in Transformers with Layer-Adjustable Interaction
Jeremiah Milbauer, Annie Louis, Mohammad Javad Hosseini +3
Transformer encoders contextualize token representations by attending to all other tokens at each layer, leading to quadratic increase in compute effort with the input length. In p…
How Does Generative Retrieval Scale to Millions of Passages?
Ronak Pradeep, Kai Hui, Jai Gupta +5
Popularized by the Differentiable Search Index, the emerging paradigm of generative retrieval re-frames the classic information retrieval problem into a sequence-to-sequence modeli…
Scaling Laws vs Model Architectures: How does Inductive Bias Influence Scaling?
Yi Tay, Mostafa Dehghani, Samira Abnar +7
There have been a lot of interest in the scaling properties of Transformer models. However, not much has been done on the front of investigating the effect of scaling properties of…