Papers with ensemble of
IIT-KGP at COIN 2019: Using pre-trained Language Models for modeling Machine Comprehension (D19-60)
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| Challenge: | Using pre-trained language models, we can model machine comprehension using commonsense reasoning. |
| Approach: | They propose a machine comprehension model that leverages pre-trained language models over commonsense knowledge bases. |
| Outcome: | The proposed model improves on baseline models and other commonsense knowledge bases. |
Assessing the Syntactic Capabilities of Transformer-based Multilingual Language Models (2021.findings-acl)
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| Challenge: | Multilingual Transformer-based language models have been shown to be excellent learners in crosslingual transfer tasks. |
| Approach: | They evaluate the syntactic generalization capabilities of BERT and RoBERTa models on English and Spanish tests. |
| Outcome: | The proposed models perform well on English and Spanish tests, and the proposed tests are compared against models on the same language and models on two different languages. |
AssistantBench: Can Web Agents Solve Realistic and Time-Consuming Tasks? (2024.emnlp-main)
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| Challenge: | Current language models and retrieval-augmented LMs are limited in their ability to perform tasks on the web. |
| Approach: | They propose a benchmark to evaluate language agents built on top of language models . they propose 'AssistantBench' which includes 214 tasks that can be automatically evaluated . |
| Outcome: | The proposed agent outperforms existing agents in a new benchmark for language agents on the web. |