Papers by Belhassen Bayar
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. |
Amory: Building Coherent Narrative-Driven Agent Memory through Agentic Reasoning (2026.eacl-long)
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| Challenge: | scalability challenges arise as conversations extend over weeks or months . current approaches fragment conversations into isolated embeddings or graph representations . |
| Approach: | They propose a working memory framework that actively constructs structured memory representations . the framework organizes conversational fragments into episodic narratives based on momentum . |
| Outcome: | Amory improves performance on LOCOMO benchmark while reducing response time by 50%. |
Distantly Supervised Transformers For E-Commerce Product QA (2021.naacl-main)
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| Challenge: | e-commerce services often provide an instant QA system on product pages . however, user queries and CQA pairs differ significantly in language characteristics . |
| Approach: | They propose a transformer-based instant question answering system on product pages . for each user query, relevant community question answer (CQA) pairs are retrieved . their framework is able to scale to large e-commerce QA traffic . |
| Outcome: | The proposed model outperforms syntactic and semantic baselines on user queries and training with CQA pairs. |