Papers by Shikhar Singh

4 papers
COM2SENSE: A Commonsense Reasoning Benchmark with Complementary Sentences (2021.findings-acl)

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Challenge: Recent advances in pretrained language models have shown promising results on commonsense reasoning benchmark datasets.
Approach: They propose a commonsense reasoning benchmark dataset with 4k sentence pairs . they propose 'gamified' model-in-the-loop setup to incentivize challenging samples .
Outcome: The proposed benchmarks show that the proposed model achieves 71% standard accuracy and 51% pairwise accuracy, well below human performance.
IndicGenBench: A Multilingual Benchmark to Evaluate Generation Capabilities of LLMs on Indic Languages (2024.acl-long)

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Challenge: IndicGenBench is the largest benchmark for evaluating large language models on user-facing generation tasks across a diverse set of 29 Indic languages .
Approach: They evaluate large language models on user-facing generation tasks across 29 languages . they use human curation to provide multi-way parallel evaluation data for many under-represented languages a github repository .
Outcome: IndicGenBench is the largest benchmark for evaluating LLMs on user-facing generation tasks across a diverse set of 29 Indic languages covering 13 scripts and 4 language families.
VIPHY: Probing “Visible” Physical Commonsense Knowledge (2023.findings-emnlp)

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Challenge: Existing studies have demonstrated that vision-language models can retain and generalize knowledge, but they do not measure their ability to retain it.
Approach: They build an automatic pipeline to derive a knowledge resource for calibrating and probing vision-language models.
Outcome: The proposed model outperforms the pretrained model on size and spatial tasks.
EventPlus: A Temporal Event Understanding Pipeline (2021.naacl-demos)

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Challenge: Event information is a type of common sense knowledge that helps people understand how stories evolve and provides predictive hints for future events.
Approach: They propose a temporal event understanding pipeline that integrates state-of-the-art components.
Outcome: The proposed pipeline can be easily adapted to other domains, including biomedical domains.

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