21 citations · 36 across the 27 of their papers we have counts for
8 papers · 1 filter
Segment-Level Diffusion: A Framework for Controllable Long-Form Generation with Diffusion Language Models
Xiaochen Zhu, Georgi Karadzhov, Chenxi Whitehouse +1
Diffusion models have shown promise in text generation, but often struggle with generating long, coherent, and contextually accurate text. Token-level diffusion doesn't model word-…
The Automated Verification of Textual Claims (AVeriTeC) Shared Task
Michael Schlichtkrull, Yulong Chen, Chenxi Whitehouse +9
The Automated Verification of Textual Claims (AVeriTeC) shared task asks participants to retrieve evidence and predict veracity for real-world claims checked by fact-checkers. Evid…
SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages
Holy Lovenia, Rahmad Mahendra, Salsabil Maulana Akbar +58
Southeast Asia (SEA) is a region rich in linguistic diversity and cultural variety, with over 1,300 indigenous languages and a population of 671 million people. However, prevailing…
CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark
David Romero, Chenyang Lyu, Haryo Akbarianto Wibowo +73
Visual Question Answering (VQA) is an important task in multimodal AI, and it is often used to test the ability of vision-language models to understand and reason on knowledge pres…
SemEval-2024 Task 8: Multidomain, Multimodel and Multilingual Machine-Generated Text Detection
Yuxia Wang, Jonibek Mansurov, Petar Ivanov +12
We present the results and the main findings of SemEval-2024 Task 8: Multigenerator, Multidomain, and Multilingual Machine-Generated Text Detection. The task featured three subtask…
PRobELM: Plausibility Ranking Evaluation for Language Models
Zhangdie Yuan, Eric Chamoun, Rami Aly +2
This paper introduces PRobELM (Plausibility Ranking Evaluation for Language Models), a benchmark designed to assess language models' ability to discern more plausible from less pla…
Towards Knowledge-Grounded Natural Language Understanding and Generation
Chenxi Whitehouse
This thesis investigates how natural language understanding and generation with transformer models can benefit from grounding the models with knowledge representations and addresse…
Inducing Generalization across Languages and Tasks using Featurized Low-Rank Mixtures
Chu-Cheng Lin, Xinyi Wang, Jonathan H. Clark +4
Adapting pretrained large language models (LLMs) to various downstream tasks in tens or hundreds of human languages is computationally expensive. Parameter-efficient fine-tuning (P…