Title |
|---|
 Exposing and Mitigating Calibration Biases and Demographic Unfairness in MLLM Few-Shot In-Context Learning for Medical Image ClassificationInternational Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2025 |
Considering Length Diversity in Retrieval-Augmented SummarizationNorth American Chapter of the Association for Computational Linguistics (NAACL), 2025 |
 Efficient Many-Shot In-Context Learning with Dynamic Block-Sparse AttentionAnnual Meeting of the Association for Computational Linguistics (ACL), 2025 |
 Aligning Black-box Language Models with Human JudgmentsNorth American Chapter of the Association for Computational Linguistics (NAACL), 2025 |
 Confidence Calibration of Classifiers with Many ClassesNeural Information Processing Systems (NeurIPS), 2024 |
 Beyond Performance: Quantifying and Mitigating Label Bias in LLMsNorth American Chapter of the Association for Computational Linguistics (NAACL), 2024 |
 Enhancing In-context Learning via Linear Probe CalibrationInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2024 |
 Promptly Predicting Structures: The Return of InferenceNorth American Chapter of the Association for Computational Linguistics (NAACL), 2024 |
 Mind Your Format: Towards Consistent Evaluation of In-Context Learning
ImprovementsAnnual Meeting of the Association for Computational Linguistics (ACL), 2024 |
 A Survey of Confidence Estimation and Calibration in Large Language
ModelsNorth American Chapter of the Association for Computational Linguistics (NAACL), 2023 |
 Gen-Z: Generative Zero-Shot Text Classification with Contextualized
Label DescriptionsInternational Conference on Learning Representations (ICLR), 2023 |
 Improving Input-label Mapping with Demonstration Replay for In-context
LearningConference on Empirical Methods in Natural Language Processing (EMNLP), 2023 |
 Generative Calibration for In-context LearningConference on Empirical Methods in Natural Language Processing (EMNLP), 2023 |
 Batch Calibration: Rethinking Calibration for In-Context Learning and
Prompt EngineeringInternational Conference on Learning Representations (ICLR), 2023 |
 Unsupervised Contrast-Consistent Ranking with Language ModelsConference of the European Chapter of the Association for Computational Linguistics (EACL), 2023 |
 Universal Self-Adaptive PromptingConference on Empirical Methods in Natural Language Processing (EMNLP), 2023 |
 A Benchmark on Extremely Weakly Supervised Text Classification:
Reconcile Seed Matching and Prompting ApproachesAnnual Meeting of the Association for Computational Linguistics (ACL), 2023 |
 Pre-Training to Learn in ContextAnnual Meeting of the Association for Computational Linguistics (ACL), 2023 |
 Parallel Context Windows for Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL), 2022 |
 Decoder Tuning: Efficient Language Understanding as DecodingAnnual Meeting of the Association for Computational Linguistics (ACL), 2022 |
 Complementary Explanations for Effective In-Context LearningAnnual Meeting of the Association for Computational Linguistics (ACL), 2022 |
 On the Relation between Sensitivity and Accuracy in In-context LearningConference on Empirical Methods in Natural Language Processing (EMNLP), 2022 |
