@inproceedings{r-etal-2025-tartantritons,
title = "{T}artan{T}ritons at {S}em{E}val-2025 Task 10: Multilingual Hierarchical Entity Classification and Narrative Reasoning using Instruct-Tuned {LLM}s",
author = "R, Raghav and
Vemali, Adarsh Prakash and
Aswal, Darpan and
Ramesh, Rahul and
Tusham, Parth and
Rishi, Pranaya",
editor = "Rosenthal, Sara and
Ros{\'a}, Aiala and
Ghosh, Debanjan and
Zampieri, Marcos",
booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.semeval-1.255/",
pages = "1964--1973",
ISBN = "979-8-89176-273-2",
abstract = "In today{'}s era of abundant online news, tackling the spread of deceptive content and manipulative narratives has become crucial. This paper details our system for SemEval-2025 Task 10, focusing on Subtasks 1 (Entity Framing) and 3 (Narrative Extraction). We instruct-tuned quantized Microsoft{'}s Phi-4 model, incorporating prompt engineering techniques to enhance performance. Our approach involved experimenting with various LLMs, including LLaMA, Phi-4, RoBERTa, and XLM-R, utilizing both quantized large models and non-quantized small models. To improve accuracy, we employed structured prompts, iterative refinement with retry mechanisms, and integrated label taxonomy information. For subtask 1, we also fine-tuned a RoBERTa classifier to predict main entity roles before classifying the fine-grained roles with Phi-4 for the English language. For subtask 3, we instruct-tuned Phi-4 to generate structured explanations, incorporating details about the article and its dominant narrative. Our system achieves competitive results in Hindi and Russian for Subtask 1."
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<abstract>In today’s era of abundant online news, tackling the spread of deceptive content and manipulative narratives has become crucial. This paper details our system for SemEval-2025 Task 10, focusing on Subtasks 1 (Entity Framing) and 3 (Narrative Extraction). We instruct-tuned quantized Microsoft’s Phi-4 model, incorporating prompt engineering techniques to enhance performance. Our approach involved experimenting with various LLMs, including LLaMA, Phi-4, RoBERTa, and XLM-R, utilizing both quantized large models and non-quantized small models. To improve accuracy, we employed structured prompts, iterative refinement with retry mechanisms, and integrated label taxonomy information. For subtask 1, we also fine-tuned a RoBERTa classifier to predict main entity roles before classifying the fine-grained roles with Phi-4 for the English language. For subtask 3, we instruct-tuned Phi-4 to generate structured explanations, incorporating details about the article and its dominant narrative. Our system achieves competitive results in Hindi and Russian for Subtask 1.</abstract>
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%0 Conference Proceedings
%T TartanTritons at SemEval-2025 Task 10: Multilingual Hierarchical Entity Classification and Narrative Reasoning using Instruct-Tuned LLMs
%A R, Raghav
%A Vemali, Adarsh Prakash
%A Aswal, Darpan
%A Ramesh, Rahul
%A Tusham, Parth
%A Rishi, Pranaya
%Y Rosenthal, Sara
%Y Rosá, Aiala
%Y Ghosh, Debanjan
%Y Zampieri, Marcos
%S Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-273-2
%F r-etal-2025-tartantritons
%X In today’s era of abundant online news, tackling the spread of deceptive content and manipulative narratives has become crucial. This paper details our system for SemEval-2025 Task 10, focusing on Subtasks 1 (Entity Framing) and 3 (Narrative Extraction). We instruct-tuned quantized Microsoft’s Phi-4 model, incorporating prompt engineering techniques to enhance performance. Our approach involved experimenting with various LLMs, including LLaMA, Phi-4, RoBERTa, and XLM-R, utilizing both quantized large models and non-quantized small models. To improve accuracy, we employed structured prompts, iterative refinement with retry mechanisms, and integrated label taxonomy information. For subtask 1, we also fine-tuned a RoBERTa classifier to predict main entity roles before classifying the fine-grained roles with Phi-4 for the English language. For subtask 3, we instruct-tuned Phi-4 to generate structured explanations, incorporating details about the article and its dominant narrative. Our system achieves competitive results in Hindi and Russian for Subtask 1.
%U https://aclanthology.org/2025.semeval-1.255/
%P 1964-1973
Markdown (Informal)
[TartanTritons at SemEval-2025 Task 10: Multilingual Hierarchical Entity Classification and Narrative Reasoning using Instruct-Tuned LLMs](https://aclanthology.org/2025.semeval-1.255/) (R et al., SemEval 2025)
ACL