EDBT 2026 Demo / reviewers in the wild / expert
Abhisheik Sharma
dblp:410/0413
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Information extraction and text analysis
narrative analysis |
1.0 | 1 | 2026 | 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 |
1.0 | 1 | 2026 | 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 |
0.9 | 1 | 2025 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | CASPER in the Machine: Insights into Character Variety in LLM-Generated StoriesabstractAs 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 ModelsabstractAnneliese 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 |