1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.CL2024★ 1 cited
Beyond Performance: Quantifying and Mitigating Label Bias in LLMs
Yuval Reif, Roy Schwartz
Large language models (LLMs) have shown remarkable adaptability to diverse tasks, by leveraging context prompts containing instructions, or minimal input-output examples. However,…
cs.CL2022
CORE: A Retrieve-then-Edit Framework for Counterfactual Data Generation
Tanay Dixit, Bhargavi Paranjape, Hannaneh Hajishirzi +1
Counterfactual data augmentation (CDA) -- i.e., adding minimally perturbed inputs during training -- helps reduce model reliance on spurious correlations and improves generalizatio…
cs.CL2021
Perturbation CheckLists for Evaluating NLG Evaluation Metrics
Ananya B. Sai, Tanay Dixit, Dev Yashpal Sheth +2
Natural Language Generation (NLG) evaluation is a multifaceted task requiring assessment of multiple desirable criteria, e.g., fluency, coherency, coverage, relevance, adequacy, ov…