Shreyas Meher
Biographic Data
| ID | 4457314 |
|---|---|
| NAME | Shreyas Meher |
| GIVEN NAMES | Shreyas |
| FAMILY NAME | Meher |
| SIGNATURE | MEHER S |
| AFFILIATIONS | The University of Texas at Dallas |
| ORCID | 0000-0002-9656-4374 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 1 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2025 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 1 |
Extractive versus Generative Language Models for Political Conflict Text Classification
We review our recent ConfliBERT language model (Hu et al . 2022 [ConfliBERT: A Pre-Trained Language Model for Political Conflict and Violence]) to process political and violence-related texts. When fine-tuned, results show that ConfliBERT has superior performance in accuracy, precision, and recall over other large language models (LLMs) like Google’s Gemma 2 (9B), Meta’s Llama 3.1 (7B), and Alibaba’s Qwen 2.5 (14B) within its relevant domains. It…
ConflLlama
We present ConflLlama, demonstrating how efficient fine-tuning of large language models can advance automated classification tasks in political science research. While classification of political events has traditionally relied on manual coding or rigid rule-based systems, modern language models offer the potential for more nuanced, context-aware analysis. However, deploying these models requires overcoming significant technical and resource barr…
ConflLlama
We present ConflLlama, demonstrating how efficient fine-tuning of large language models can advance automated classification tasks in political science research. While classification of political events has traditionally relied on manual coding or rigid rule-based systems, modern language models offer the potential for more nuanced, context-aware analysis. However, deploying these models requires overcoming significant technical and resource barr…
Extractive versus Generative Language Models for Political Conflict Text Classification
We review our recent ConfliBERT language model (Hu et al . 2022 [ConfliBERT: A Pre-Trained Language Model for Political Conflict and Violence]) to process political and violence-related texts. When fine-tuned, results show that ConfliBERT has superior performance in accuracy, precision, and recall over other large language models (LLMs) like Google’s Gemma 2 (9B), Meta’s Llama 3.1 (7B), and Alibaba’s Qwen 2.5 (14B) within its relevant domains. It…
ConflLlama
We present ConflLlama, demonstrating how efficient fine-tuning of large language models can advance automated classification tasks in political science research. While classification of political events has traditionally relied on manual coding or rigid rule-based systems, modern language models offer the potential for more nuanced, context-aware analysis. However, deploying these models requires overcoming significant technical and resource barr…
Adaptation (eye (1 works) · Artificial Intelligence (1 works) · Computational and Text Analysis Methods (1 works) · Computer Science (1 works) · Domain (mathematical analysis (1 works) · Domain adaptation (1 works) · Event (particle physics (1 works) · Generative grammar (1 works) · Generative model (1 works) · Hate Speech and Cyberbullying Detection (1 works)