Faisal Ladhak
6 papers in the PaperMetrix corpus
Papers by this author
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Spurious Correlations in Reference-Free Evaluation of Text Generation
2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Model-based, reference-free evaluation metrics have been proposed as a fast and cost-effective approach to evaluate Natural Language Generation (NLG) systems. Despite promising recent results, we find evidence that reference-free evaluation metrics of summarization and dialog …
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Contrastive Error Attribution for Finetuned Language Models
2022 · arXiv (Cornell University)
Recent work has identified noisy and misannotated data as a core cause of hallucinations and unfaithful outputs in Natural Language Generation (NLG) tasks. Consequently, identifying and removing these examples is a key open challenge in …
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Incorporating Human Explanations for Robust Hate Speech Detection
2024 · arXiv (Cornell University)
Given the black-box nature and complexity of large transformer language models (LM), concerns about generalizability and robustness present ethical implications for domains such as hate speech (HS) detection. Using the content rich Social Bias Frames …
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A neural interlingua for multilingual machine translation
2018
We incorporate an explicit neural interlingua into a multilingual encoder-decoder neural machine translation (NMT) architecture. We demonstrate that our model learns a languageindependent representation by performing direct zero-shot translation (without using pivot translation), and by …
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The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
2021
Sebastian Gehrmann, Tosin Adewumi, Karmanya Aggarwal, Pawan Sasanka Ammanamanchi, Anuoluwapo Aremu, Antoine Bosselut, Khyathi Raghavi Chandu, Miruna-Adriana Clinciu, Dipanjan Das, Kaustubh Dhole, Wanyu Du, Esin Durmus, Ondřej Dušek, Chris Chinenye Emezue, Varun Gangal, Cristina Garbacea, …
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Benchmarking Large Language Models for News Summarization
2024 · Transactions of the Association for Computational Linguistics
Abstract Large language models (LLMs) have shown promise for automatic summarization but the reasons behind their successes are poorly understood. By conducting a human evaluation on ten LLMs across different pretraining methods, prompts, and model …