24 citations · 36 across the 29 of their papers we have counts for
6 papers · 1 filter
Understanding the Logic of Direct Preference Alignment through Logic
Kyle Richardson, Vivek Srikumar, Ashish Sabharwal
Recent direct preference alignment algorithms (DPA), such as DPO, have shown great promise in aligning large language models to human preferences. While this has motivated the deve…
State Space Models are Strong Text Rerankers
Zhichao Xu, Jinghua Yan, Ashim Gupta +1
Transformers dominate NLP and IR; but their inference inefficiencies and challenges in extrapolating to longer contexts have sparked interest in alternative model architectures. Am…
Beyond Perplexity: Multi-dimensional Safety Evaluation of LLM Compression
Zhichao Xu, Ashim Gupta, Tao Li +2
Increasingly, model compression techniques enable large language models (LLMs) to be deployed in real-world applications. As a result of this momentum towards local deployment, com…
An Empirical Investigation of Matrix Factorization Methods for Pre-trained Transformers
Ashim Gupta, Sina Mahdipour Saravani, P. Sadayappan +1
The increasing size of transformer-based models in NLP makes the question of compressing them important. In this work, we present a comprehensive analysis of factorization based mo…
In-Context Example Ordering Guided by Label Distributions
Zhichao Xu, Daniel Cohen, Bei Wang +1
By allowing models to predict without task-specific training, in-context learning (ICL) with pretrained LLMs has enormous potential in NLP. However, a number of problems persist in…
Promptly Predicting Structures: The Return of Inference
Maitrey Mehta, Valentina Pyatkin, Vivek Srikumar
Prompt-based methods have been used extensively across NLP to build zero- and few-shot label predictors. Many NLP tasks are naturally structured: that is, their outputs consist of…