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20242026
most citedInternLM2 Technical Report

29 citations · 29 across the 13 of their papers we have counts for

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Showing 2024Show all

5 papers · 1 filter

cs.CL2024

Evaluating Robustness of LLMs on Crisis-Related Microblogs across Events, Information Types, and Linguistic Features

Muhammad Imran, Abdul Wahab Ziaullah, Kai Chen +1

The widespread use of microblogging platforms like X (formerly Twitter) during disasters provides real-time information to governments and response authorities. However, the data f…

cs.CL2024

Input Conditioned Graph Generation for Language Agents

Lukas Vierling, Jie Fu, Kai Chen

Recent progress in Large Language Models (LLMs) and language agents has demonstrated significant promise for various future applications across multiple disciplines. While traditio…

cs.CL2024

ABEX: Data Augmentation for Low-Resource NLU via Expanding Abstract Descriptions

Sreyan Ghosh, Utkarsh Tyagi, Sonal Kumar +4

We present ABEX, a novel and effective generative data augmentation methodology for low-resource Natural Language Understanding (NLU) tasks. ABEX is based on ABstract-and-EXpand, a…

cs.CV2024

GenEARL: A Training-Free Generative Framework for Multimodal Event Argument Role Labeling

Hritik Bansal, Po-Nien Kung, P. Jeffrey Brantingham +2

Multimodal event argument role labeling (EARL), a task that assigns a role for each event participant (object) in an image is a complex challenge. It requires reasoning over the en…

cs.CL202429 cited

InternLM2 Technical Report

Zheng Cai, Maosong Cao, Haojiong Chen +97

The evolution of Large Language Models (LLMs) like ChatGPT and GPT-4 has sparked discussions on the advent of Artificial General Intelligence (AGI). However, replicating such advan…