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COLING 2025: all accepted papers

848 papers · page 3 of 9 · the most cited first · ← COLING 2025

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  1. Deploying Multi-task Online Server with Large Language Model ↗Yincen Qu, Hengyue Liu, Kun Wang et al. · Industry Track
  2. Dialectal and Low Resource Machine Translation for Aromanian ↗Alexandru-Iulius Jerpelea, Alina Radoi, Sergiu Nisioi
  3. Do LLMs Know When to NOT Answer? Investigating Abstention Abilities of Large Language Models ↗Nishanth Madhusudhan, Sathwik Tejaswi Madhusudhan, Vikas Yadav et al.
  4. Dying or Departing? Euphemism Detection for Death Discourse in Historical Texts ↗Ali Al-Laith, Alexander Conroy, Jens Bjerring-Hansen et al.
  5. EDAR: A pipeline for Emotion and Dialogue Act Recognition ↗Elie Dina, Rania Ayachi Kibech, Miguel Couceiro · Industry Track
  6. Efficient Tool Use with Chain-of-Abstraction Reasoning ↗Silin Gao, Jane Dwivedi-Yu, Ping Yu et al.
  7. Efficient Vocabulary Reduction for Small Language Models ↗Yuta Nozaki, Dai Nakashima, Ryo Sato et al. · Industry Track
  8. Engagement-driven Persona Prompting for Rewriting News Tweets ↗Reshmi Gopalakrishna Pillai, Antske Fokkens, Wouter van Atteveldt
  9. Enhancing Discourse Parsing for Local Structures from Social Media with LLM-Generated Data ↗Martial Pastor, Nelleke Oostdijk, Patricia Martin-Rodilla et al.
  10. Enhancing Retrieval-Augmented Generation: A Study of Best Practices ↗Siran Li, Linus Stenzel, Carsten Eickhoff et al.
  11. Evaluating Pixel Language Models on Non-Standardized Languages ↗Alberto Muñoz-Ortiz, Verena Blaschke, Barbara Plank
  12. Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding ↗Tadesse Destaw Belay, Israel Abebe Azime, Abinew Ali Ayele et al.
  13. Evaluating the Consistency of LLM Evaluators ↗Noah Lee, Jiwoo Hong, James Thorne
  14. Exploring Backdoor Vulnerabilities of Chat Models ↗Wenkai Yang, Yunzhuo Hao, Yankai Lin
  15. Exploring Fine-Grained Human Motion Video Captioning ↗Bingchan Zhao, Xinyi Liu, Zhuocheng Yu et al.
  16. Exploring Language Model Generalization in Low-Resource Extractive QA ↗Saptarshi Sengupta, Wenpeng Yin, Preslav Nakov et al.

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