Karishma Sharma — accepted AI-conference papers

2025

Tuning-Free Personalized Alignment via Trial-Error-Explain In-Context Learning

Hyundong Justin Cho, Karishma Sharma, Nicolaas Paul Jedema, Leonardo F. R. Ribeiro, Jonathan May, Alessandro Moschitti

NAACL 2025findings

Language models are aligned to the collective voice of many, resulting in generic outputs that do not align with specific users’ styles. In this work, we present Trial-Error-Explain In-Context Learning (TICL), a tuning-free method that personalizes language models for text generation tasks with fewe…

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2024

Efficient and Accurate Contextual Re-Ranking for Knowledge Graph Question Answering

Kexuan Sun, Nicolaas Paul Jedema, Karishma Sharma, Ruben Janssen, Jay Pujara, Pedro Szekely +1

COLING 2024main

The efficacy of neural “retrieve and generate” systems is well established for question answering (QA) over unstructured text. Recent efforts seek to extend this approach to knowledge graph (KG) QA by converting structured triples to unstructured text. However, the relevance of KG triples retrieved…

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2024

Speechworthy Instruction-tuned Language Models

Hyundong Justin Cho, Nicolaas Paul Jedema, Leonardo F. R. Ribeiro, Karishma Sharma, Pedro Szekely, Alessandro Moschitti +2

EMNLP 2024main

Current instruction-tuned language models are exclusively trained with textual preference data and thus may not be aligned to the unique requirements of other modalities, such as speech. To better align language models with the speech domain, we explore i) prompting strategies based on radio-industr…

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2021

VigDet: Knowledge Informed Neural Temporal Point Process for Coordination Detection on Social Media

Yizhou Zhang, Karishma Sharma, Yan Liu

NeurIPS 2021poster

Recent years have witnessed an increasing use of coordinated accounts on social media, operated by misinformation campaigns to influence public opinion and manipulate social outcomes. Consequently, there is an urgent need to develop an effective methodology for coordinated group detection to combat…

Cited by 34 Source ↗PDF ↗Scholar ↗

2020

NoiseRank: Unsupervised Label Noise Reduction with Dependence Models

Karishma Sharma, Pinar Donmez, Enming Luo, Yan Liu, I. Zeki Yalniz

ECCV 2020poster

Label noise is increasingly prevalent in datasets acquired from noisy channels. Existing approaches that detect and remove label noise generally rely on some form of supervision, which is not scalable and error-prone. In this paper, we propose NoiseRank, for unsupervised label noise reduction using…

Cited by 43 Source ↗PDF ↗Scholar ↗