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1. Learning Which Features Matter: RoBERTa Acquires a Preference for Linguistic Generalizations (Eventually)

   https://doi.org/10.18653/v1/2020.emnlp-main.16  

   Warstadt, Alex; Zhang, Yian; Li, Xiaocheng; Liu, Haokun; Bowman, Samuel R. (January 2020, Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP))

   null (Ed.)

   One reason pretraining on self-supervised linguistic tasks is effective is that it teaches models features that are helpful for language understanding. However, we want pretrained models to learn not only to represent linguistic features, but also to use those features preferentially during fine-turning. With this goal in mind, we introduce a new English-language diagnostic set called MSGS (the Mixed Signals Generalization Set), which consists of 20 ambiguous binary classification tasks that we use to test whether a pretrained model prefers linguistic or surface generalizations during finetuning. We pretrain RoBERTa from scratch on quantities of data ranging from 1M to 1B words and compare their performance on MSGS to the publicly available RoBERTa_BASE. We find that models can learn to represent linguistic features with little pretraining data, but require far more data to learn to prefer linguistic generalizations over surface ones. Eventually, with about 30B words of pretraining data, RoBERTa_BASE does consistently demonstrate a linguistic bias with some regularity. We conclude that while self-supervised pretraining is an effective way to learn helpful inductive biases, there is likely room to improve the rate at which models learn which features matter. 

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2. Coloring the blank slate: Pre-training imparts a hierarchical inductive bias to sequence-to-sequence models

   https://doi.org/10.18653/v1/2022.findings-acl.106  

   Mueller, Aaron; Frank, Robert; Linzen, Tal; Wang, Luheng; Schuster, Sebastian (May 2022, Findings of the Association for Computational Linguistics)

   Relations between words are governed by hierarchical structure rather than linear ordering. Sequence-to-sequence (seq2seq) models, despite their success in downstream NLP applications, often fail to generalize in a hierarchy sensitive manner when performing syntactic transformations—for example, transforming declarative sentences into questions. However, syntactic evaluations of seq2seq models have only observed models that were not pre-trained on natural language data before being trained to perform syntactic transformations, in spite of the fact that pre-training has been found to induce hierarchical linguistic generalizations in language models; in other words, the syntactic capabilities of seq2seq models may have been greatly understated. We address this gap using the pre-trained seq2seq models T5 and BART, as well as their multilingual variants mT5 and mBART. We evaluate whether they generalize hierarchically on two transformations in two languages: question formation and passivization in English and German. We find that pre-trained seq2seq models generalize hierarchically when performing syntactic transformations, whereas models trained from scratch on syntactic transformations do not. This result presents evidence for the learnability of hierarchical syntactic information from non-annotated natural language text while also demonstrating that seq2seq models are capable of syntactic generalization, though only after exposure to much more language data than human learners receive. 

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3. seqLens : optimizing language models for genomic predictions

   https://doi.org/10.1101/2025.03.12.642848  

   Baghbanzadeh, Mahdi; Mann, Brendan; Crandall, Keith A; Rahnavard, Ali (March 2025, bioRxiv)

   Understanding genomic sequences through the lens of language modeling has the potential to revolutionize biological research, yet challenges in tokenization, model architecture, and adaptation to diverse genomic contexts remain. In this study, we investigated key innovations in DNA sequence modeling, treating DNA as a language and applying language models to genomic data. We gathered two diverse pretraining datasets: one consisting of 19,551 reference genomes, including over 18,000 prokaryotic genomes (115B nucleotides), and another more balanced dataset with 1,354 genomes, including 1,166 prokaryotic and 188 eukaryotic reference genomes (180B nucleotides). We trained five byte-pair encoding tokenizers and pretrained 52 DNA language models, systematically comparing different architectures, hyperparameters, and classification heads. We introduceseqLens, a family of models based on disentangled attention with relative positional encoding, which outperforms state-of-the-art models in 13 of 19 benchmarking phenotypic predictions. We further explore continual pretraining, domain adaptation, and parameter-efficient fine-tuning methods to assess trade-offs between computational efficiency and accuracy. Our findings demonstrate that relevant pretraining data significantly boosts performance, alternative pooling techniques enhance classification, and larger tokenizers negatively impact generalization. These insights provide a foundation for optimizing DNA language models and improving genome annotations. 

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4. The lottery ticket hypothesis for pre-trained BERT networks

   Chen, Tianlong; Frankle, Jonathan; Chang, Shiyu; Liu, Sijia; Zhang, Yang; Wang, Zhangyang; Carbin, Michael (December 2020, Advances in neural information processing systems)

   null (Ed.)

   In natural language processing (NLP), enormous pre-trained models like BERT have become the standard starting point for training on a range of downstream tasks, and similar trends are emerging in other areas of deep learning. In parallel, work on the lottery ticket hypothesis has shown that models for NLP and computer vision contain smaller matching subnetworks capable of training in isolation to full accuracy and transferring to other tasks. In this work, we combine these observations to assess whether such trainable, transferrable subnetworks exist in pre-trained BERT models. For a range of downstream tasks, we indeed find matching subnetworks at 40% to 90% sparsity. We find these subnetworks at (pre-trained) initialization, a deviation from prior NLP research where they emerge only after some amount of training. Subnetworks found on the masked language modeling task (the same task used to pre-train the model) transfer universally; those found on other tasks transfer in a limited fashion if at all. As large-scale pre-training becomes an increasingly central paradigm in deep learning, our results demonstrate that the main lottery ticket observations remain relevant in this context. 

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5. DiffSLVA: Harnessing Diffusion Models for Sign Language Video Anonymization

   Xia, Zhaoyang; Neidle, Carol; Metaxas, Dimitris N. (November 2023, arXiv)

   Since American Sign Language (ASL) has no standard written form, Deaf signers frequently share videos in order to communicate in their native language. However, since both hands and face convey critical linguistic information in signed languages, sign language videos cannot preserve signer privacy. While signers have expressed interest, for a variety of applications, in sign language video anonymization that would effectively preserve linguistic content, attempts to develop such technology have had limited success, given the complexity of hand movements and facial expressions. Existing approaches rely predominantly on precise pose estimations of the signer in video footage and often require sign language video datasets for training. These requirements prevent them from processing videos 'in the wild,' in part because of the limited diversity present in current sign language video datasets. To address these limitations, our research introduces DiffSLVA, a novel methodology that utilizes pre-trained large-scale diffusion models for zero-shot text-guided sign language video anonymization. We incorporate ControlNet, which leverages low-level image features such as HED (Holistically-Nested Edge Detection) edges, to circumvent the need for pose estimation. Additionally, we develop a specialized module dedicated to capturing facial expressions, which are critical for conveying essential linguistic information in signed languages. We then combine the above methods to achieve anonymization that better preserves the essential linguistic content of the original signer. This innovative methodology makes possible, for the first time, sign language video anonymization that could be used for real-world applications, which would offer significant benefits to the Deaf and Hard-of-Hearing communities. We demonstrate the effectiveness of our approach with a series of signer anonymization experiments. 

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