Abhisheik Sharma

dblp:410/0413 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2026
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 2 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
2 papers
Language models and text generation · 50% Information extraction and text analysis · 50%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Natural language and speech › Information extraction and text analysis
narrative analysis
1.012026
CASPER in the Machine: Insights into Character Variety in LLM-Generated Stories · ACL (1) 2026
Natural language and speech › Language models and text generation › text generation
story generation
1.012026
CASPER in the Machine: Insights into Character Variety in LLM-Generated Stories · ACL (1) 2026
Natural language and speech › Language models and text generation
large language model
0.912025
Classifying Unreliable Narrators with Large Language Models · ACL (1) 2025

Methods — techniques the papers use, named apart from their topics

large language model · 1.9
YearPublicationVenuePosition
2026 CASPER in the Machine: Insights into Character Variety in LLM-Generated Stories
abstract
As LLM-generated text is increasingly used, especially in fictional domains, we explore how much LLM-generated stories differ from human-written stories.In this work, we focus on characters.We borrow definitions from narratology to analyze 8 intricate category-pairs of character, such as stylization and wholeness.These category-pairs consider more than just basic characteristics.They assess how characters are portrayed within their stories.After automatically inferring categories of characters within both LLM and human-written stories, we compare and contrast these two sets of stories.We consider the following overarching questions: (1) Do LLMs and human-written stories have similar characters? and (2) Do LLMs generate stories with a variety of characters?Our analysis includes research questions that focus on stories generated by popular LLMs and recently published human-written stories.We describe a number of interesting similarities, differences and key takeaways.1
Anneliese Brei, Abhisheik Sharma, Nicholas Sanaie, Lu Wang 0008, Snigdha Chaturvedi
ACL (1)2
2025 Classifying Unreliable Narrators with Large Language Models
abstract
Anneliese Brei, Katharine Henry, Abhisheik Sharma, Shashank Srivastava, Snigdha Chaturvedi. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.
Anneliese Brei, Katharine Henry, Abhisheik Sharma, Snigdha Chaturvedi
ACL (1)3