Papers with language model representations
Pre-trained language model representations for language generation (N19-1)
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| Challenge: | Pre-trained language model representations have been successful in a wide range of language understanding tasks. |
| Approach: | They propose to use pre-trained language model representations to integrate them into sequence to sequence models and apply it to machine translation and abstractive summarization. |
| Outcome: | The proposed model is able to perform 5.3 BLEU in machine translation and 5.3 on the full text version of CNN/DailyMail. |
Speech language models lack important brain-relevant semantics (2024.acl-long)
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| Challenge: | Recent work shows that text-based language models predict both text- and speech-evoked brain activity. |
| Approach: | They remove low-level stimulus features from language models to assess their impact on alignment with fMRI brain recordings during reading and listening. |
| Outcome: | The proposed model removes low-level features from fMRI brain recordings to assess their impact on alignment with fmr recordings. |
On Robustness of Finetuned Transformer-based NLP Models (2023.findings-emnlp)
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| Challenge: | Pretrained Transformer-based language models have been finetuned for a large number of tasks. |
| Approach: | They characterize changes between pretrained and finetuned models with CKA and STIR metrics. |
| Outcome: | The proposed models are more robust to perturbations than BERT and T5 on classification tasks and generation tasks. |
Probing for Reading Times (2026.acl-long)
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Eleftheria Tsipidi, Samuel Kiegeland, Francesco Ignazio Re, Tianyang Xu, Mario Giulianelli, Karolina Stanczak, Ryan Cotterell
| Challenge: | a large body of work on probing has demonstrated that language model representations encode a wealth of linguistic information, but it remains unclear whether they also capture cognitive signals about human processing. |
| Approach: | They use regularized linear regression to compare language model representations against scalar predictors. |
| Outcome: | The representations from early layers outperform surprisal in predicting early-pass measures such as first fixation and gaze duration. |
Document-Level Event Role Filler Extraction using Multi-Granularity Contextualized Encoding (2020.acl-main)
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| Challenge: | Document-level event extraction requires a view of a larger context to determine which spans of text correspond to event role fillers. |
| Approach: | They propose a multi-granularity reader to dynamically aggregate information captured by neural representations learned at different levels of granularities. |
| Outcome: | The proposed model performs substantially better than previous models on the MUC-4 event extraction dataset. |