activity
20232026
most citedInstruct and Extract: Instruction Tuning for On-Demand Information Extraction

1 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

cs.IR2026

RAG without Forgetting: Continual Query-Infused Key Memory

Yuntong Hu, Sha Li, Naren Ramakrishnan +1

Retrieval-augmented generation (RAG) systems commonly improve robustness via query-time adaptations such as query expansion and iterative retrieval. While effective, these approach…

cs.AI2026★ 1 cited

Exploring LLMs for Scientific Information Extraction Using The SciEx Framework

Sha Li, Ayush Sadekar, Nathan Self +10

Large language models (LLMs) are increasingly touted as powerful tools for automating scientific information extraction. However, existing methods and tools often struggle with the…

cs.CL2025

FanChuan: A Multilingual and Graph-Structured Benchmark For Parody Detection and Analysis

Yilun Zheng, Sha Li, Fangkun Wu +9

Parody is an emerging phenomenon on social media, where individuals imitate a role or position opposite to their own, often for humor, provocation, or controversy. Detecting and an…

cs.CL2023

Defining a New NLP Playground

Sha Li, Chi Han, Pengfei Yu +8

The recent explosion of performance of large language models (LLMs) has changed the field of Natural Language Processing (NLP) more abruptly and seismically than any other shift in…

cs.CL2023★ 1 cited

Instruct and Extract: Instruction Tuning for On-Demand Information Extraction

Yizhu Jiao, Ming Zhong, Sha Li +4

Large language models with instruction-following capabilities open the door to a wider group of users. However, when it comes to information extraction - a classic task in natural…