What Can We Learn from Collective Human Opinions on Natural Language Inference Data? (2020.emnlp-main)
Copied to clipboard
| Challenge: | Despite the subjective nature of many NLU evaluations, little attention has been paid to the distribution of human opinions. |
| Approach: | They use a dataset with 464,500 annotations to study Collective HumAn OpinionS . they argue that models lack the ability to recover the distribution over human labels . |
| Outcome: | The proposed dataset examines the distribution of human opinions in NLU evaluation datasets. |
Similar Papers
Distributed NLI: Learning to Predict Human Opinion Distributions for Language Reasoning (2022.findings-acl)
Copied to clipboard
| Challenge: | Using distributed NLI, we show that models can capture human judgement distribution more effectively than the softmax baseline. |
| Approach: | They propose a new NLU task to predict the distribution of human judgements . they propose Monte Carlo, Deep Ensemble, Re-Calibration and Distribution Distillation methods to capture human judgement distributions. |
| Outcome: | The proposed methods perform better than the softmax baseline, but the results are still far below the estimated human upper-bound. |
Capture Human Disagreement Distributions by Calibrated Networks for Natural Language Inference (2022.findings-acl)
Copied to clipboard
| Challenge: | Previously, it's common to disregard it as noise or as a sign of poor-quality data, as their annotations are heavily based on personal experience and opinions. |
| Approach: | They propose to capture the human disagreement distribution from the perspective of model calibration. |
| Outcome: | The proposed model can achieve competitive performance when well-calibrated, on divergence scores between predictive probability and the true human opinion distribution, and the accuracy. |
What Ingredients Make for an Effective Crowdsourcing Protocol for Difficult NLU Data Collection Tasks? (2021.acl-long)
Copied to clipboard
| Challenge: | Despite the importance of datasets for natural language understanding, there has been little attention on crowdsourcing methods for collecting datasets. |
| Approach: | They compare the effectiveness of crowdsourcing methods for boosting NLU example difficulty with training crowdworkers instead of expert judgments. |
| Outcome: | The proposed method is ineffective for boosting NLU example difficulty, but it is not effective for training crowdworkers and qualifying workers based on expert judgments. |
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. |
Collective Human Opinions in Semantic Textual Similarity (2023.tacl-1)
Copied to clipboard
| Challenge: | Existing benchmarks for semantic textual similarity (STS) use averaged human ratings as gold standard. |
| Approach: | They propose to use a Chinese sentence-to-sentence dataset to study collective human opinions in semantic textual similarity (STS) neither a scalar nor a single Gaussian fits a set of observed judgments adequately, they argue . |
| Outcome: | The proposed dataset does not capture disagreements on individual instances, but rather the confidence over the aggregate dataset. |
“Seeing the Big through the Small”: Can LLMs Approximate Human Judgment Distributions on NLI from a Few Explanations? (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Human label variation arises when multiple human annotators provide different labels for valid reasons. |
| Approach: | They propose to use crowd workers to represent human judgment distributions or expert linguists to provide detailed explanations for their chosen labels. |
| Outcome: | The proposed model can approximate human judgment distributions using a small number of expert labels and explanations. |
LLMs instead of Human Judges? A Large Scale Empirical Study across 20 NLP Evaluation Tasks (2025.acl-short)
Copied to clipboard
Anna Bavaresco, Raffaella Bernardi, Leonardo Bertolazzi, Desmond Elliott, Raquel Fernández, Albert Gatt, Esam Ghaleb, Mario Giulianelli, Michael Hanna, Alexander Koller, Andre Martins, Philipp Mondorf, Vera Neplenbroek, Sandro Pezzelle, Barbara Plank, David Schlangen, Alessandro Suglia, Aditya K Surikuchi, Ece Takmaz, Alberto Testoni
| Challenge: | Existing evaluations of NLP models with LLMs are based on human judgments . however, there are concerns about their validity and reproducibility in proprietary models . |
| Approach: | They evaluate 11 current LLMs for their ability to replicate annotations. they show substantial variance across models and datasets. |
| Outcome: | The proposed model can replicate human annotations on 20 NLP datasets and show substantial variance across models and datasets. |
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 . |
Ecologically Valid Explanations for Label Variation in NLI (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Human label variation exists in many natural language processing tasks, including NLI . |
| Approach: | They build an English dataset of 1,415 ecologically valid explanations for 122 MNLI items . they find that people can systematically vary on their interpretation . |
| Outcome: | The proposed dataset contains 1,415 ecologically valid explanations for 122 items . the results show that people can vary on interpretation and highlight differences . |
OCNLI: Original Chinese Natural Language Inference (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Recent efforts to extend natural language understanding to other languages have focused on (automatically) translating existing English datasets. |
| Approach: | They propose to use a Chinese dataset to generate annotated sentences from native speakers specializing in linguistics to elicit annotations. |
| Outcome: | The proposed dataset does not rely on automatic translation or non-expert annotation. instead, it elicits annotations from native speakers specializing in linguistics. |