Jay DeYoung, Sarthak Jain, Nazneen Fatema Rajani, Eric Lehman, Caiming Xiong, Richard Socher, Byron C. Wallace
| Challenge: | State-of-the-art models in NLP are opaque in terms of how they come to make predictions. |
| Approach: | They propose to release a benchmark to measure the quality of rationales extracted by models and how faithful these rationale are to human annotators. |
| Outcome: | The proposed benchmark will enable researchers to compare models and track progress on interpretable models for NLP. |
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| Challenge: | Abstract: Large language models are gaining widespread adoption in natural language processing tasks. |
| Approach: | They propose a semi-supervised approach to optimize for plausibility of extracted rationales by using a pre-trained natural language inference model and a supervised NLI predictor. |
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Goodhart’s Law Applies to NLP’s Explanation Benchmarks (2024.findings-eacl)
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| Challenge: | Popular methods for "explaining" the outputs of natural language processing (NLP) models operate by highlighting a subset of input tokens that ought, in some sense, to be salient. |
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Model Interpretability and Rationale Extraction by Input Mask Optimization (2023.findings-acl)
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| Challenge: | Existing methods for creating explanations for black-box models struggle with deriving easily interpretable explanations. |
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A Tutorial on Evaluation Metrics used in Natural Language Generation (2021.naacl-tutorials)
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| Challenge: | This tutorial presents the evolution of automatic evaluation metrics to their current state along with emerging trends in this field. |
| Approach: | This tutorial presents the evolution of automatic evaluation metrics to their current state . it aims to assess the extent of scientific progress made and identify areas/components that need improvement . |
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Did the Models Understand Documents? Benchmarking Models for Language Understanding in Document-Level Relation Extraction (2023.acl-long)
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| Challenge: | Document-level relation extraction (DocRE) models achieve consistent performance gains in DocRE, but their underlying decision rules are still understudied. |
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Interpretation of NLP models through input marginalization (2020.emnlp-main)
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| Challenge: | Existing methods to interpret NLP predictions replace each token with a predefined value, resulting in misleading interpretations. |
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| Challenge: | General-purpose Language Models have changed the world of Natural Language Processing, if not the world itself. |
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Evaluation Metrics in the Era of GPT-4: Reliably Evaluating Large Language Models on Sequence to Sequence Tasks (2023.emnlp-main)
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| Challenge: | Large Language Models (LLMs) evaluation is a patchy and inconsistent landscape . established automatic evaluation metrics are poor surrogates, correlating weakly with human judgement. |
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DHP Benchmark: Are LLMs Good NLG Evaluators? (2025.findings-naacl)
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Yicheng Wang, Jiayi Yuan, Yu-Neng Chuang, Zhuoer Wang, Yingchi Liu, Mark Cusick, Param Kulkarni, Zhengping Ji, Yasser Ibrahim, Xia Hu
| Challenge: | Large Language Models (LLMs) are increasingly serving as evaluators in Natural Language Generation (NLG) tasks. |
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