activity
20162025
most citedDataless Knowledge Fusion by Merging Weights of Language Models

9 citations · 14 across the 8 of their papers we have counts for

collaborators

12 papers

cs.DB2025

STARQA: A Question Answering Dataset for Complex Analytical Reasoning over Structured Databases

Mounica Maddela, Lingjue Xie, Daniel Preotiuc-Pietro +1

Semantic parsing methods for converting text to SQL queries enable question answering over structured data and can greatly benefit analysts who routinely perform complex analytics…

cs.CL2023

Leveraging Contextual Information for Effective Entity Salience Detection

Rajarshi Bhowmik, Marco Ponza, Atharva Tendle +5

In text documents such as news articles, the content and key events usually revolve around a subset of all the entities mentioned in a document. These entities, often deemed as sal…

cs.LG2023

Unsupervised Contrast-Consistent Ranking with Language Models

Niklas Stoehr, Pengxiang Cheng, Jing Wang +2

Language models contain ranking-based knowledge and are powerful solvers of in-context ranking tasks. For instance, they may have parametric knowledge about the ordering of countri…

cs.CL2023★ 1 cited

Overcoming Catastrophic Forgetting in Massively Multilingual Continual Learning

Genta Indra Winata, Lingjue Xie, Karthik Radhakrishnan +5

Real-life multilingual systems should be able to efficiently incorporate new languages as data distributions fed to the system evolve and shift over time. To do this, systems need…

cs.CL2022★ 9 cited

Dataless Knowledge Fusion by Merging Weights of Language Models

Xisen Jin, Xiang Ren, Daniel Preotiuc-Pietro +1

Fine-tuning pre-trained language models has become the prevalent paradigm for building downstream NLP models. Oftentimes fine-tuned models are readily available but their training…

cs.CL2022

EntSUM: A Data Set for Entity-Centric Summarization

Mounica Maddela, Mayank Kulkarni, Daniel Preotiuc-Pietro

Controllable summarization aims to provide summaries that take into account user-specified aspects and preferences to better assist them with their information need, as opposed to…