83 citations · 118 across the 11 of their papers we have counts for
12 papers
DAGGER: Distractor-Aware Graph Generation for Executable Reasoning in Math Problems
Zabir Al Nazi, Shubhashis Roy Dipta, Sudipta Kar
Chain-of-Thought (CoT) prompting is widely adopted for mathematical problem solving, including in low-resource languages, yet its behavior under irrelevant context remains underexp…
Chain-of-Instructions: Compositional Instruction Tuning on Large Language Models
Shirley Anugrah Hayati, Taehee Jung, Tristan Bodding-Long +4
Fine-tuning large language models (LLMs) with a collection of large and diverse instructions has improved the model's generalization to different tasks, even for unseen tasks. Howe…
Integrating Summarization and Retrieval for Enhanced Personalization via Large Language Models
Chris Richardson, Yao Zhang, Kellen Gillespie +5
Personalization, the ability to tailor a system to individual users, is an essential factor in user experience with natural language processing (NLP) systems. With the emergence of…
MultiCoNER v2: a Large Multilingual dataset for Fine-grained and Noisy Named Entity Recognition
Besnik Fetahu, Zhiyu Chen, Sudipta Kar +2
We present MULTICONER V2, a dataset for fine-grained Named Entity Recognition covering 33 entity classes across 12 languages, in both monolingual and multilingual settings. This da…
SemEval-2023 Task 2: Fine-grained Multilingual Named Entity Recognition (MultiCoNER 2)
Besnik Fetahu, Sudipta Kar, Zhiyu Chen +2
We present the findings of SemEval-2023 Task 2 on Fine-grained Multilingual Named Entity Recognition (MultiCoNER 2). Divided into 13 tracks, the task focused on methods to identify…
Preventing Catastrophic Forgetting in Continual Learning of New Natural Language Tasks
Sudipta Kar, Giuseppe Castellucci, Simone Filice +2
Multi-Task Learning (MTL) is widely-accepted in Natural Language Processing as a standard technique for learning multiple related tasks in one model. Training an MTL model requires…