Papers with NPI
Masked Language Model Scoring (2020.acl-main)
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| Challenge: | Pretrained masked language models require finetuning for most tasks. |
| Approach: | They evaluate pretrained masked language models out of the box via their pseudo-log-likelihood scores (PLLs) they attribute this success to PLL’s unsupervised expression of linguistic acceptability without a left-to-right bias, greatly improving on scores from GPT-2 . |
| Outcome: | The proposed model outperforms autoregressive language models in a variety of tasks. |
Investigating BERT’s Knowledge of Language: Five Analysis Methods with NPIs (D19-1)
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Alex Warstadt, Yu Cao, Ioana Grosu, Wei Peng, Hagen Blix, Yining Nie, Anna Alsop, Shikha Bordia, Haokun Liu, Alicia Parrish, Sheng-Fu Wang, Jason Phang, Anhad Mohananey, Phu Mon Htut, Paloma Jeretic, Samuel R. Bowman
| Challenge: | Recent work evaluating sentence representation models' knowledge of grammar has been slower to emerge. |
| Approach: | They propose five experimental methods inspired by prior work evaluating pretrained sentence representation models to examine their grammatical knowledge. |
| Outcome: | The proposed methods show that the model has significant knowledge of the licensing environment but its success varies widely across different methods. |
Few-Shot Complex Knowledge Base Question Answering via Meta Reinforcement Learning (2020.emnlp-main)
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| Challenge: | Existing approaches to complex question-answering (CQA) exhibit uneven performance when questions have different types, harboring inherently different characteristics, e.g., difficulty level. |
| Approach: | They propose a meta-reinforcement learning approach to program induction in CQA to tackle the potential distributional bias in questions. |
| Outcome: | The proposed method achieves state-of-the-art performance on the CQA dataset while using only five trial trajectories for the top-5 retrieved questions in each support set. |
The effects of distance on NPI illusive effects in BERT (2024.emnlp-main)
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| Challenge: | Recent studies have examined the syntactic capabilities of pre-trained language models, such as BERT. |
| Approach: | They examine the syntactic capabilities of pre-trained language models by using psycholinguistic stimuli. |
| Outcome: | The proposed model is highly sensitive to hierarchical or linear information compared to hierarical layers . |
Fine-Grained Analysis of Shared Syntactic Mechanisms in Language Models (2026.acl-long)
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| Challenge: | Recent advances in language models have demonstrated their ability to process linguistic expressions with complex syntactic structures. |
| Approach: | They investigate whether language models employ shared neural mechanisms across different constructions by applying causal interpretability methods at a granular level. |
| Outcome: | The proposed model performance improves on acceptability judgment benchmarks. |