Papers with WinoGrande
MALLM: Multi-Agent Large Language Models Framework (2025.emnlp-demos)
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| Challenge: | Multi-agent debate (MAD) has demonstrated the ability to augment collective intelligence by scaling test-time compute and leveraging expertise. |
| Approach: | They propose an open-source framework that enables systematic analysis of multi-agent debates. |
| Outcome: | The proposed framework enables systematic analysis of multi-agent debate components. |
On Curriculum Learning for Commonsense Reasoning (2022.naacl-main)
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| Challenge: | Recent research suggests that data order can have a significant impact on the performance of finetuned models for natural language understanding. |
| Approach: | They use paced curriculum learning to rank data and sample training mini-batches with increasing levels of difficulty during finetuning. |
| Outcome: | The proposed model improves performance for socialIQA, CosmosQA, CODAH, HellaSwag, WinoGrande in both tuning settings. |
Generative Data Augmentation for Commonsense Reasoning (2020.findings-emnlp)
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Yiben Yang, Chaitanya Malaviya, Jared Fernandez, Swabha Swayamdipta, Ronan Le Bras, Ji-Ping Wang, Chandra Bhagavatula, Yejin Choi, Doug Downey
| Challenge: | Recent advances in commonsense reasoning depend on large-scale human-authored training data. |
| Approach: | They propose a generative data augmentation technique that augments human-authored training data by using pretrained language models. |
| Outcome: | The proposed technique outperforms existing methods on commonsense reasoning benchmarks and enhances out-of-distribution generalization. |
Unsupervised Pronoun Resolution via Masked Noun-Phrase Prediction (2021.acl-short)
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| Challenge: | Masked Noun-Phrase Prediction (MNPP) is a pre-training strategy to tackle pronoun resolution in a fully unsupervised setting. |
| Approach: | They propose a pre-training strategy to tackle pronoun resolution in an unsupervised setting by fine-tuning a large pre-trained model on a human-labeled dataset and then transferring to a smaller dataset such as Winograd Schema Challenge (WSC). |
| Outcome: | The proposed method outperforms all previous unsupervised methods on all datasets by large margins. |
In Benchmarks We Trust ... Or Not? (2025.emnlp-main)
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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. |