Daniel Khashabi Source Confirmed

Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.

High Impact

Assistant Professor

John Brown University

faculty

34 h-index 171 pubs 5,003 cited

Research Areas

Topic Modeling Natural Language Processing Techniques Advanced Text Analysis Techniques Multimodal Machine Learning Applications Software Engineering Research

Links

ORCID

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OverviewAI-generated summary

Dr. Daniel Khashabi is an assistant professor at John Brown University whose work encompasses natural language processing and multimodal machine learning. He explores topic modeling and advanced text analysis techniques, with a particular emphasis on the capabilities and limitations of large language models. Khashabi's research includes studies on aligning language models with self-generated instructions and when not to trust language models, investigating the effectiveness of parametric and non-parametric memories. His work also considers cross-task generalization via natural language crowdsourcing instructions and question answering benchmarks requiring implicit reasoning.

Metrics

  • h-index: 34
  • Publications: 171
  • Citations: 5,003

Selected Publications

  • SELF-[IN]CORRECT: LLMs Struggle with Discriminating Self-Generated Responses (2025) DOI
  • CARDBiomedBench: A Benchmark for Evaluating Large Language Model Performance in Biomedical Research (2025) DOI
  • “According to . . . ”: Prompting Language Models Improves Quoting from Pre-Training Data (2024) DOI
  • AnaloBench: Benchmarking the Identification of Abstract and Long-context Analogies (2024) DOI
  • Insights into LLM Long-Context Failures: When Transformers Know but Don’t Tell (2024) DOI
  • Controllable Safety Alignment: Inference-Time Adaptation to Diverse Safety Requirements (2024) DOI
  • k-SemStamp: A Clustering-Based Semantic Watermark for Detection of Machine-Generated Text (2024) DOI
  • The Language Barrier: Dissecting Safety Challenges of LLMs in Multilingual Contexts (2024) DOI
  • SemStamp: A Semantic Watermark with Paraphrastic Robustness for Text Generation (2024) DOI
  • SELF-[IN]CORRECT: LLMs Struggle with Discriminating Self-Generated Responses (2024) DOI
  • Dated Data: Tracing Knowledge Cutoffs in Large Language Models (2024) DOI
  • k-SemStamp: A Clustering-Based Semantic Watermark for Detection of Machine-Generated Text (2024) DOI
  • The Language Barrier: Dissecting Safety Challenges of LLMs in Multilingual Contexts (2024) DOI
  • SemStamp: A Semantic Watermark with Paraphrastic Robustness for Text Generation (2023) DOI
  • Self-Instruct: Aligning Language Models with Self-Generated Instructions (2023) DOI

Collaborators

Researchers in the database who share publications

Yeganeh Kordi John Brown University 4 shared publications Adam Byerly John Brown University 1 shared publication Yi‐Zhong Wang John Brown University 1 shared publication

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