Papers by Prakhar Ganesh
Compressing Large-Scale Transformer-Based Models: A Case Study on BERT (2021.tacl-1)
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Prakhar Ganesh, Yao Chen, Xin Lou, Mohammad Ali Khan, Yin Yang, Hassan Sajjad, Preslav Nakov, Deming Chen, Marianne Winslett
| Challenge: | Popular pre-trained Transformers have improved performance for various NLP tasks by sizable margins, but are too resource-hungry and computation-intensive to suit low-capacity devices or applications with strict latency requirements. |
| Approach: | They present a literature review of the compression of Transformers, focusing on the popular BERT model, which has attracted considerable research attention. |
| Outcome: | The proposed models improve Sentiment analysis, paraphrase detection, machine reading comprehension, question answering, text summarization, and other tasks by sizable margins. |
Say It Another Way: Auditing LLMs with a User-Grounded Automated Paraphrasing Framework (2026.eacl-long)
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Clea Chataigner, Rebecca Ma, Prakhar Ganesh, Yuhao Chen, Afaf Taik, Elliot Creager, Golnoosh Farnadi
| Challenge: | Existing studies have studied prompt sensitivity by altering formatting or generating paraphrases with automated techniques. |
| Approach: | They propose a framework for generating controlled paraphrases grounded in user behaviors . they leverage linguistically informed rules and enforce quality through checks on instruction adherence . |
| Outcome: | The proposed framework is able to detect weaknesses in large language models . it leverages linguistically informed rules and enforces quality through checks on instruction adherence, semantic similarity, and realism. |
Rethinking Hallucinations: Correctness, Consistency, and Prompt Multiplicity (2026.eacl-long)
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| Challenge: | Existing hallucination evaluations focus only on correctness and often overlook consistency . a significant inconsistency in benchmarks like Med-HALT suggests hallucianation-related harms have been misunderstood. |
| Approach: | They propose a framework for quantifying consistency in hallucination evaluations . they find that detection techniques detect consistency, not correctness . |
| Outcome: | The proposed framework uncovers critical limitations in hallucination evaluations. |