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Abhishek Kumar

3 papers hereh-index 3166 citations10 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • sole author1
  • first author1
  • middle author1

Across the 3 of 3 papers where every author was matched, so the position is known.

fields
  • cs.AI1
  • cs.CL1
  • cs.CV1
same name
  • Abhishek Kumar — 6 papers, h 2
  • Abhishek Kumar — 5 papers, h 1
  • Abhishek Kumar — 5 papers, h 2
  • Abhishek Kumar — 5 papers, h 2
  • Abhishek Kumar — 5 papers, h 1
  • Abhishek Kumar — 4 papers, h 1

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2025

Through the Prism of Culture: Evaluating LLMs' Understanding of Indian Subcultures and Traditions

Garima Chhikara, Abhishek Kumar, Abhijnan Chakraborty

Large Language Models (LLMs) have shown remarkable advancements but also raise concerns about cultural bias, often reflecting dominant narratives at the expense of under-represente…

cs.CL2024

Found in the Middle: Calibrating Positional Attention Bias Improves Long Context Utilization

Cheng-Yu Hsieh, Yung-Sung Chuang, Chun-Liang Li +8

Large language models (LLMs), even when specifically trained to process long input contexts, struggle to capture relevant information located in the middle of their input. This phe…

cs.CL2024

Confidence Under the Hood: An Investigation into the Confidence-Probability Alignment in Large Language Models

Abhishek Kumar, Robert Morabito, Sanzhar Umbet +2

As the use of Large Language Models (LLMs) becomes more widespread, understanding their self-evaluation of confidence in generated responses becomes increasingly important as it is…

cs.CL2024

Subtle Biases Need Subtler Measures: Dual Metrics for Evaluating Representative and Affinity Bias in Large Language Models

Abhishek Kumar, Sarfaroz Yunusov, Ali Emami

Research on Large Language Models (LLMs) has often neglected subtle biases that, although less apparent, can significantly influence the models' outputs toward particular social na…

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