Precise Task Formalization Matters in Winograd Schema Evaluations (2020.emnlp-main)
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| Challenge: | Recent results report a surge in performance to nearhuman levels on the Winograd Schema Challenge (WSC) however, variations in task formulation across papers and evaluations makes it hard to understand the true degree of recent progress. |
| Approach: | They propose to use a model with multiple choice to frame the task as multiple choice and reuse a pretrained language modeling head to mitigate the model's extreme sensitivity to hyperparameters. |
| Outcome: | The proposed frameworks improve the model's reasoning ability by framing the task as multiple choice and reuse of a pretrained language modeling head. |
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| Challenge: | Large Language Models excel at the Winograd Schema Challenge, but struggle with instances that feature minor alterations or rewording. |
| Approach: | They propose an open-source platform that harnesses a human-in-the-loop approach to create a dynamic dataset tailored to such altered WSC instances. |
| Outcome: | The proposed model outperforms existing models in the Winograd Schema Challenge (WSC) a human-in-the-loop approach allows for a dynamic dataset tailored to such altered instances. |
An Analysis of Dataset Overlap on Winograd-Style Tasks (2020.coling-main)
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| Challenge: | a large number of test instances overlap considerably with pretraining corpora, a study finds . for a number of years, models struggled to exceed chance-level performance . |
| Approach: | They analyze the effects of varying degrees of overlaps that occur between pretraining corpora and test instances in WSC-style tasks. |
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Back to Square One: Artifact Detection, Training and Commonsense Disentanglement in the Winograd Schema (2021.emnlp-main)
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| Challenge: | Pre-trained language models have boosted performance on some WS benchmarks, but the source of improvement is not clear. |
| Approach: | They propose a method that uses twin sentences for evaluation and two new baselines that account for artifacts in WS benchmarks. |
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A Surprisingly Robust Trick for the Winograd Schema Challenge (P19-1)
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| Challenge: | The Winograd Schema Challenge (WSC) dataset WSC273 and its inference counterpart WNLI are popular benchmarks for natural language understanding and commonsense reasoning. |
| Approach: | They propose to fine-tune language models on the Winograd Schema Challenge dataset WSC273 and its inference counterpart WNLI to achieve accuracies of 72.5% and 74.7%, respectively. |
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How Reasonable are Common-Sense Reasoning Tasks: A Case-Study on the Winograd Schema Challenge and SWAG (D19-1)
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| Challenge: | a recent study has improved the state-of-the-art on common-sense reasoning benchmarks . a san francisco-based approach to common-ense reasoning is challenging . |
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Wino-X: Multilingual Winograd Schemas for Commonsense Reasoning and Coreference Resolution (2021.emnlp-main)
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| Challenge: | Winograd schemas are well-established tools for evaluating coreference resolution and commonsense reasoning capabilities of computational models. |
| Approach: | They present a dataset of German, French, and Russian schemas aligned with their English counterparts. |
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The Sensitivity of Language Models and Humans to Winograd Schema Perturbations (2020.acl-main)
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| Challenge: | Large-scale pre-trained language models are driving recent improvements in perfromance on the Winograd Schema Challenge . a diagnostic dataset shows that these models are sensitive to linguistic perturbations that minimally affect human understanding . |
| Approach: | They propose to use a dataset to test pre-trained language models for the Winograd Schema Challenge . they show that these models are sensitive to linguistic perturbations that minimally affect human understanding . |
| Outcome: | The proposed models are sensitive to linguistic perturbations that minimally affect human understanding. |
WSC+: Enhancing The Winograd Schema Challenge Using Tree-of-Experts (2024.eacl-long)
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| Challenge: | Large Language Models (LLMs) excel at answering WSC questions, but their ability to generate such questions remains less explored. |
| Approach: | They propose a tree-of-experts prompting method which enhances the generation of WSC instances by incorporating new 'ambiguous' and 'offensive' categories. |
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Combining Knowledge Hunting and Neural Language Models to Solve the Winograd Schema Challenge (P19-1)
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| Challenge: | Existing methods to solve Winograd Schema Challenge use only knowledge embedded in text . this limits the performance of such models on the WSC problems. |
| Approach: | They propose to augment existing language models with a commonsense knowledge hunting module and an explicit reasoning module to extract the needed knowledge from text. |
| Outcome: | The proposed system improves on the language model based methods by 5.53% and 7.7% on the dataset. |
From Informal to Formal – Incorporating and Evaluating LLMs on Natural Language Requirements to Verifiable Formal Proofs (2025.acl-long)
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Jialun Cao, Yaojie Lu, Meiziniu Li, Haoyang Ma, Haokun Li, Mengda He, Cheng Wen, Le Sun, Hongyu Zhang, Shengchao Qin, Shing-Chi Cheung, Cong Tian
| Challenge: | Recent studies in formal mathematical reasoning have shown an unstoppable growth trend. |
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