Papers by Muhammad Bilal Zafar
Do LLM hallucination detectors suffer from low-resource effect? (2026.eacl-long)
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| Challenge: | a long line of work suggests that LLMs face issues along both dimensions . |
| Approach: | They investigate hallucination detectors' failure modes and their effects on the task accuracy of four LLMs and three halluciner detectors. |
| Outcome: | The models show impressive performance in high-resource languages like English but the performance degrades significantly in low-resourced languages like Bengali. |
On the Lack of Robust Interpretability of Neural Text Classifiers (2021.findings-acl)
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Muhammad Bilal Zafar, Michele Donini, Dylan Slack, Cedric Archambeau, Sanjiv Das, Krishnaram Kenthapadi
| Challenge: | Several models have been proposed to interpret models with feature-based interpretability methods. |
| Approach: | They propose to quantify the robustness of neural text classifiers by using two randomization tests to compare models with identical initializations. |
| Outcome: | The proposed methods show surprising deviations from expected behavior . the results raise questions about the extent of insights that practitioners may draw from interpretations. |
Can LLMs Explain Themselves Counterfactually? (2025.emnlp-main)
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| Challenge: | Explanations are an important tool for gaining insights into model behavior, calibrating user trust, and ensuring compliance. |
| Approach: | They propose to use self-explanation to prompt models to explain outputs . they find that LLMs struggle to generate SCEs - their prediction often does not agree with their own counterfactual reasoning. |
| Outcome: | The proposed methods can generate SCEs across families, sizes, temperatures, and datasets. |
The Impact of Inference Acceleration on Bias of LLMs (2025.naacl-long)
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| Challenge: | Recent work suggests strategies to increase inference efficiency with LLMs . however, these strategies may inadvertently lead to some side-effects. |
| Approach: | They propose to optimize inference acceleration strategies such as quantization, pruning, and caching to reduce inference cost and latency while maintaining predictive performance. |
| Outcome: | The proposed strategies reduce cost and latency while maintaining predictive performance while preserving the model size. |
Characterizing Web Search in The Age of Generative AI (2026.findings-acl)
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Elisabeth Kirsten, Jost Große Perdekamp, Qinyuan Wu, Mihir Upadhyay, Krishna P. Gummadi, Muhammad Bilal Zafar
| Challenge: | generative search is a new search paradigm that uses LLMs to retrieve information from the web . traditional web search returns a ranked list of independent web pages . |
| Approach: | They compare generative search with traditional web search, which returns ranked results as a list of independent web pages. |
| Outcome: | The results show that generative search systems achieve topical coverage comparable to traditional search, but differ in retrieval footprints and synthesis strategies. |