Papers by Shadi Iskander
Leveraging Prototypical Representations for Mitigating Social Bias without Demographic Information (2024.naacl-short)
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| Challenge: | Existing approaches to mitigate social biases require explicit annotation of demographic information for each sample. |
| Approach: | They propose a method that leverages predefined demographic texts and incorporates a regularization term during the fine-tuning process to mitigate bias in language models. |
| Outcome: | The proposed method outperforms debiasing methods with limited demographic-annotated data. |
HotelQuEST: Balancing Quality and Efficiency in Agentic Search (2026.eacl-industry)
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| Challenge: | Existing benchmarks for agentic search focus primarily on answer quality, overlooking efficiency factors that are critical for real-world deployment. |
| Approach: | They propose a benchmark for hotel search queries that includes 214 hotel query queries that range from simple factual requests to complex queries. |
| Outcome: | The proposed benchmarks show that LLM-based agents achieve higher accuracy than traditional retrievers, but at substantially higher costs due to redundant tool calls and suboptimal routing that fails to match query complexity to model capability. |
Shielded Representations: Protecting Sensitive Attributes Through Iterative Gradient-Based Projection (2023.findings-acl)
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| Challenge: | Natural language processing models tend to learn and encode social biases present in the data. |
| Approach: | They propose a method for removing non-linear encoded concepts from neural representations by iteratively training neural classifiers to predict a particular attribute, followed by a projection of the representation on a hypersurface. |
| Outcome: | The proposed method removes non-linear encoded concepts from neural representations. |
Quality Matters: Evaluating Synthetic Data for Tool-Using LLMs (2024.emnlp-main)
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| Challenge: | Existing methods to assess data quality for training and testing large language models are lacking. |
| Approach: | They propose two approaches to assess the reliability of data for training large language models for external tool usage. |
| Outcome: | The proposed approaches outperform models trained on high-quality data on two popular benchmarks and an extrinsic evaluation that showcases the impact of data quality on model performance. |