Papers by Suraj Maharjan
Scalable Prompt Generation for Semi-supervised Learning with Language Models (2023.findings-eacl)
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| Challenge: | Prompt-based learning methods in semi-supervised learning (SSL) settings have been shown to be effective on multiple natural language understanding datasets and tasks. |
| Approach: | They propose to use a set of prompt tokens to create diverse prompt models and a varying number of soft prompt token to encourage language models to learn different prompts. |
| Outcome: | The proposed method achieves the best average accuracy of 71.5% in different few-shot learning settings. |
Folksonomication: Predicting Tags for Movies from Plot Synopses using Emotion Flow Encoded Neural Network (C18-1)
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| Challenge: | Existing systems that generate tags for movies can help users better retrieve movies based on their personal preferences and user profiles. |
| Approach: | They propose a neural network model that merges synopses and emotion flows to predict a set of movies' tags. |
| Outcome: | The proposed model outperforms a machine learning system by learning 18% more tags than the previous one. |
Disentangling Biased Knowledge from Reasoning in Large Language Models via Machine Unlearning (2025.acl-long)
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Zheyuan Liu, Suraj Maharjan, Fanyou Wu, Rahil Parikh, Belhassen Bayar, Srinivasan H. Sengamedu, Meng Jiang
| Challenge: | Existing approaches to disentangle biased knowledge from reasoning are sub-optimal . entangled data makes curation difficult, leading to inclusion of sensitive, toxic data. |
| Approach: | They propose a framework that selectively removes biased knowledge while preserving reasoning abilities. |
| Outcome: | The proposed framework improves fairness accuracy by 14.7% and reasoning performance by 62.6% across multiple LLMs. |
A Genre-Aware Attention Model to Improve the Likability Prediction of Books (D18-1)
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| Challenge: | Existing methods for likability prediction are time-consuming and too rigid. |
| Approach: | They propose a novel neural architecture that incorporates genre supervision to assign weights to individual feature types based on the characteristics of each book. |
| Outcome: | The proposed method outperforms state-of-the-art methods and achieves competitive results. |
Feedback-Aware Prompt Optimization Framework for Generating Job Postings (2026.eacl-industry)
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| Challenge: | Creating high-quality job postings is time-consuming and requires significant time from hiring managers and recruiters. |
| Approach: | They propose a feedback-aware prompt optimization framework that automates high-quality job posting generation through iterative human-in-the-loop refinement. |
| Outcome: | The proposed framework shows high compliance rates and strong satisfaction scores in both automated and human evaluations. |
Letting Emotions Flow: Success Prediction by Modeling the Flow of Emotions in Books (N18-2)
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| Challenge: | We obtained the best weighted F1-score of 69% for predicting books’ success in a multitask setting. |
| Approach: | They propose to model the flow of emotions over a book using recurrent neural networks and quantify its usefulness in predicting success in books. |
| Outcome: | The proposed model obtained the best weighted F1-score of 69% for predicting books’ success in a multitask setting. |
Comprehensive Layer-wise Analysis of SSL Models for Audio Deepfake Detection (2025.findings-naacl)
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| Challenge: | Existing algorithms for audio deepfake detection are based on layer-wise analysis of self-supervised learning (SSL) models. |
| Approach: | They conduct a layer-wise analysis of self-supervised learning (SSL) models for audio deepfake detection across diverse contexts. |
| Outcome: | The proposed models achieve competitive equal error rate (EER) scores even when employing a reduced number of layers. |
MPST: A Corpus of Movie Plot Synopses with Tags (L18-1)
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| Challenge: | a corpus of movie plot synopses and tags can be used to build automatic tagging systems . a method to collect these tags allows us to learn to predict tags from plot synoopsis . |
| Approach: | They propose to collect a corpus of movie plot synopses and 70 tags to analyze their properties. |
| Outcome: | The proposed method can be used to predict movie tags from plot synopses. |