What Will it Take to Fix Benchmarking in Natural Language Understanding? (2021.naacl-main)
Copied to clipboard
| 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 . |
| Outcome: | The proposed frameworks fail at four criteria, and adversarial data collection does not address the causes of these failures, the authors argue . restoring a healthy evaluation ecosystem will require significant progress in the design of benchmark datasets, reliability with which they are annotated, their size, and the ways they handle social bias. |
Similar Papers
Adversarial NLI: A New Benchmark for Natural Language Understanding (2020.acl-main)
Copied to clipboard
| Challenge: | a new large-scale NLI benchmark dataset is presented to test models on a variety of popular NLIs. |
| Approach: | They propose a large-scale NLI benchmark dataset that is iteratively compared with a human-and-model-in-the-loop procedure. |
| Outcome: | The proposed method can be applied in a never-ending learning scenario, becoming a moving target for NLU, rather than a static benchmark that will quickly saturate. |
Targeting the Benchmark: On Methodology in Current Natural Language Processing Research (2021.acl-short)
Copied to clipboard
| Challenge: | a language benchmark is a task devised that is restricted enough to be managable with current methods, but is deemed challenging enough to serve as a benchmark. |
| Approach: | They propose to use a language task as a benchmark and a baseline model to argue it is challenging enough to be a good one. |
| Outcome: | The proposed language benchmarks are based on a dataset and a language task . the proposed benchmarks can be used to measure progress towards the goal of the research . |
A Tutorial on Evaluation Metrics used in Natural Language Generation (2021.naacl-tutorials)
Copied to clipboard
| 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 . |
| Outcome: | This tutorial presents the evolution of automatic evaluation metrics to their current state along with emerging trends in this field. |
In Benchmarks We Trust ... Or Not? (2025.emnlp-main)
Copied to clipboard
Ine Gevers, Victor De Marez, Jens Van Nooten, Jens Lemmens, Andriy Kosar, Ehsan Lotfi, Nikolay Banar, Pieter Fivez, Luna De Bruyne, Walter Daelemans
| Challenge: | Existing benchmarks for Large Language Models (LLMs) are inadequate and lack a clear solution. |
| Approach: | They propose checklists to cover all aspects of benchmarking issues, both for benchmark creation and usage. |
| Outcome: | The proposed checklists cover all aspects of benchmarking issues, both for benchmark creation and usage. |
FewNLU: Benchmarking State-of-the-Art Methods for Few-Shot Natural Language Understanding (2022.acl-long)
Copied to clipboard
Yanan Zheng, Jing Zhou, Yujie Qian, Ming Ding, Chonghua Liao, Li Jian, Ruslan Salakhutdinov, Jie Tang, Sebastian Ruder, Zhilin Yang
| Challenge: | Existing evaluation protocols for few-shot natural language understanding (NLU) tasks are inconsistent and hinder fair comparison and measuring progress. |
| Approach: | They propose an evaluation framework that improves previous evaluation procedures in three key aspects, i.e., test performance, dev-test correlation, and stability. |
| Outcome: | The proposed framework improves evaluation procedures in three key aspects, i.e., performance, dev-test correlation, and stability. |
Navigating the Modern Evaluation Landscape: Considerations in Benchmarks and Frameworks for Large Language Models (LLMs) (2024.lrec-tutorials)
Copied to clipboard
| Challenge: | General-purpose Language Models have changed the world of Natural Language Processing, if not the world itself. |
| Approach: | This tutorial will lay the foundations and explain the basics of evaluation and compare traditional methods to newly developed methods. |
| Outcome: | The tutorial assumes little familiarity with metrics, datasets, prompts and benchmarks . it will compare traditional methods to newly developed methods . |
MENLI: Robust Evaluation Metrics from Natural Language Inference (2023.tacl-1)
Copied to clipboard
| Challenge: | Recent proposed BERT-based evaluation metrics for text generation are vulnerable to adversarial attacks, e.g., relating to information correctness. |
| Approach: | They propose to use BERT-based evaluation metrics for text generation to evaluate text for semantic similarity but are vulnerable to adversarial attacks using Natural Language Inference. |
| Outcome: | The proposed metrics outperform existing summarization metrics but perform below SOTA MT metrics on standard benchmarks. |
Deconstructing NLG Evaluation: Evaluation Practices, Assumptions, and Their Implications (2022.naacl-main)
Copied to clipboard
| Challenge: | Evaluating natural language generation systems is difficult, as there are many ways to express similar things in text. |
| Approach: | They combine interviews with NLG practitioners to examine ethical considerations and their implications for NLG evaluation. |
| Outcome: | The findings of the study surface goals, community practices, assumptions, and constraints that shape NLG evaluations, and examine their implications and how they embody ethical considerations. |
A guide to the dataset explosion in QA, NLI, and commonsense reasoning (2020.coling-tutorials)
Copied to clipboard
| Challenge: | a tutorial aims to provide an up-to-date guide to the recent datasets . the target audience is the NLP practitioners who are lost in dozens of the recent data sets. |
| Approach: | This tutorial provides an up-to-date guide to the recent datasets . it surveys old and new methodological issues with dataset construction . |
| Outcome: | This tutorial aims to provide an up-to-date guide to the recent datasets . it surveys the old and new methodological issues with dataset construction . |
JGLUE: Japanese General Language Understanding Evaluation (2022.lrec-1)
Copied to clipboard
| Challenge: | There is no benchmark for Japanese to evaluate and analyze NLU ability from different perspectives. |
| Approach: | They build a Japanese NLU benchmark from scratch without translation to measure general NLU ability in Japanese. |
| Outcome: | a Japanese NLU benchmark is built from scratch without translation to measure general NLU ability in Japanese. |