The Curse of Performance Instability in Analysis Datasets: Consequences, Source, and Suggestions (2020.emnlp-main)
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| Challenge: | Neural network models have significantly pushed forward performance on natural language processing benchmarks with the development of largescale language model pre-training. |
| Approach: | They find that models on natural language inference and reading comprehension are unstable . they propose to use a model-selection routine to analyze the model's instability . |
| Outcome: | The proposed models can perform poorly on two language-related tasks, the authors show . they also show that the model selection routine is unstable, and that it is not reliable . |
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| Challenge: | Many previous studies have investigated fine-tuning pre-trained language models on downstream tasks with varying random seeds, but they only used the standard deviation of performance scores (SD) as their measure, which is a narrow characterization of instability. |
| Approach: | They propose a systematic evaluation framework for the standard deviation of performance scores (SD) and six other measures quantifying instability of different granularity levels. |
| Outcome: | The proposed framework will be used to evaluate the validity of these measures and to improve them. |
Behavior Analysis of NLI Models: Uncovering the Influence of Three Factors on Robustness (N18-1)
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| Challenge: | Currently, state-of-the-art models achieve impressive test set performance in the form of accuracy scores. |
| Approach: | They examine the models' robustness to semantically-valid alterations to the input data by identifying three factors and comparing their impact on three SNLI models. |
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What Will it Take to Fix Benchmarking in Natural Language Understanding? (2021.naacl-main)
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| Challenge: | Evaluation for many natural language understanding (NLU) tasks is broken due to unreliable and biased systems scoring so high on standard benchmarks. |
| Approach: | They argue that current benchmarks fail at four criteria for evaluation . they argue that adversarial data collection does not address the causes of failures . |
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A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendations (2024.emnlp-main)
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Md Tahmid Rahman Laskar, Sawsan Alqahtani, M Saiful Bari, Mizanur Rahman, Mohammad Abdullah Matin Khan, Haidar Khan, Israt Jahan, Amran Bhuiyan, Chee Wei Tan, Md Rizwan Parvez, Enamul Hoque, Shafiq Joty, Jimmy Huang
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SCORE: Systematic COnsistency and Robustness Evaluation for Large Language Models (2025.naacl-industry)
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| Challenge: | Typical evaluations of Large Language Models (LLMs) report a single accuracy metric per dataset, often derived from an optimized setup. |
| Approach: | They propose a framework for non-adversarial evaluation of large language models that evaluates models by repeatedly testing them on the same benchmarks in various setups. |
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Morphological Inflection: A Reality Check (2023.acl-long)
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| Challenge: | Morphological inflection is a popular task in sub-word NLP with practical and cognitive applications. |
| Approach: | They propose new methods to analyze data sets and evaluate their generalization abilities to better reflect likely use-cases. |
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Let’s Stop Incorrect Comparisons in End-to-end Relation Extraction! (2020.emnlp-main)
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| Challenge: | Existing literature on Relation Extraction (RE) uses multiple evaluation setups to compare performance. |
| Approach: | They propose to quantify the most common comparison mistake and evaluate it leads to overestimating the final RE performance by around 5% on ACE05. |
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Evaluating the Robustness of Neural Language Models to Input Perturbations (2021.emnlp-main)
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| Challenge: | High-performance neural language models have achieved state-of-the-art results on a wide range of NLP tasks, but results for common benchmark datasets often do not reflect model reliability and robustness when applied to noisy, real-world data. |
| Approach: | They propose to implement character-level and word-level perturbation methods to simulate scenarios in which input texts may be slightly noisy or different from the data distribution on which NLP systems were trained. |
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Robustness and Adversarial Examples in Natural Language Processing (2021.emnlp-tutorials)
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| Challenge: | This tutorial aims to raise awareness of practical concerns about NLP robustness . it aims at addressing the weaknesses of NLP systems when faced with adversarial inputs and data with a distribution shift . |
| Approach: | This tutorial aims to bring awareness of practical concerns about NLP robustness . it reviews recent studies on analyzing the weakness of NLP systems when facing adversarial inputs . |
| Outcome: | This tutorial aims to bring awareness of practical concerns about NLP robustness . it will examine the weaknesses of NLP systems when faced with adversarial inputs and data with a distribution shift . |
Whispers of Doubt Amidst Echoes of Triumph in NLP Robustness (2024.naacl-long)
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| Challenge: | Existing approaches to measure robustness are problematic, and out-of-domain evaluations are no longer relevant. |
| Approach: | They examine models of different sizes spanning different architectural choices and pretraining objectives. |
| Outcome: | The results show that not all out-of-domain tests provide insight into robustness . merely scaling models does not make them adequately robust . |