Papers with human-level accuracy
Unsupervised Question Answering for Fact-Checking (D19-66)
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| Challenge: | Recent Deep Learning (DL) models have achieved human-level accuracy on natural language tasks such as question-answering, natural language inference, and textual entailment. |
| Approach: | They propose an unsupervised question-answering based approach for a similar task, fact-checking. |
| Outcome: | The proposed approach achieves label accuracy of 80.2% on the development set and 80.25% on the test set. |
Improving the Robustness of QA Models to Challenge Sets with Variational Question-Answer Pair Generation (2021.acl-srw)
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| Challenge: | Existing data augmentation methods for reading comprehension lack robustness to challenge sets whose distribution is different from that of training sets. |
| Approach: | They propose a question-answer pair generation method that generates multiple diverse QA pairs from a paragraph to mitigate this problem. |
| Outcome: | The proposed model improves the accuracy of 12 challenge sets and the in-distribution accuracy. |
Can Multimodal Large Language Models Understand Spatial Relations? (2025.acl-long)
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| Challenge: | Spatial relation reasoning is a crucial task for multimodal large language models to understand the objective world. |
| Approach: | They propose a human-annotated spatial relation reasoning benchmark based on COCO2017 to improve MLLMs' spatial relation thinking. |
| Outcome: | The proposed benchmark achieves 48.14% accuracy, far below the human-level accuracy of 98.40%. |
SaRoCo: Detecting Satire in a Novel Romanian Corpus of News Articles (2021.acl-short)
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| Challenge: | a corpus for satire detection in Romanian news is based on satirical reporting . the goal is to ridicule public figures, politics or contemporary events . |
| Approach: | They propose a corpus for satire detection in Romanian news . they gather 55,608 public news articles from multiple real and satirical sources . |
| Outcome: | The proposed corpus is one of the largest corpora for satire detection regardless of language . it is the only one for the Romanian language, and the results show that it is low on the machine level compared to human level . |
CIVET: Systematic Evaluation of Understanding in VLMs (2025.findings-emnlp)
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Massimo Rizzoli, Simone Alghisi, Olha Khomyn, Gabriel Roccabruna, Seyed Mahed Mousavi, Giuseppe Riccardi
| Challenge: | Current Vision-Language Models can accurately recognize only a limited set of basic object properties; 3) they struggle to understand basic relations among objects. |
| Approach: | They propose a framework that evaluates VLMs on exhaustive sets of stimuli, free from annotation noise, dataset-specific biases, and uncontrolled scene complexity. |
| Outcome: | The proposed framework addresses the lack of standardized systematic evaluation for assessing VLMs’ understanding, enabling researchers to test hypotheses with statistical rigor. |
Modelling Commonsense Properties Using Pre-Trained Bi-Encoders (2022.coling-1)
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| Challenge: | Pre-trained language models can capture commonsense properties that are rarely expressed in text. |
| Approach: | They propose to fine-tune language models to explicitly model commonsense properties . they train separate concept and property encoders on extracted hyponym-hypernym pairs and generic sentences . |
| Outcome: | The proposed model can capture commonsense properties with higher accuracy than human models . a new study shows that the model can model commonsensence properties with much higher accuracy . |
Hollywood Identity Bias Dataset: A Context Oriented Bias Analysis of Movie Dialogues (2022.lrec-1)
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Sandhya Singh, Prapti Roy, Nihar Sahoo, Niteesh Mallela, Himanshu Gupta, Pushpak Bhattacharyya, Milind Savagaonkar, Nidhi Sultan, Roshni Ramnani, Anutosh Maitra, Shubhashis Sengupta
| Challenge: | Movies reflect society and also hold power to transform opinions. |
| Approach: | They propose to annotate movie scripts for identity bias using a dataset that is annotated for gender, race/ethnicity, religion, age, occupation, LGBTQ, and other . |
| Outcome: | The proposed dataset contains dialogue turns annotated for gender, race/ethnicity, religion, age, occupation, LGBTQ, and other, which contains biases like body shaming, personality bias, etc. |
Quantile Regression with Large Language Models for Price Prediction (2025.findings-acl)
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| Challenge: | Existing approaches to structured prediction tasks focus on point estimates and lack systematic comparison across different methods. |
| Approach: | They propose a novel quantile regression approach that enables LLMs to produce full predictive distributions, improving upon traditional point estimates. |
| Outcome: | The proposed model outperforms encoder architectures, embedding-based methods, and few-shot learning methods in prediction accuracy and distributional calibration. |