| Challenge: | A multi-modal neural architecture outperforms its equivalent trained on language alone with a 2% decrease in perplexity . |
| Approach: | They propose to use visual context to train neural language models to perform next-word prediction. |
| Outcome: | The proposed model outperforms its equivalent trained on language with 2% decrease in perplexity even when no visual context is available at test. |
Similar Papers
Visual Grounding Helps Learn Word Meanings in Low-Data Regimes (2024.naacl-long)
Copied to clipboard
| Challenge: | Modern neural language models (LMs) require distinctly un-human-like ways to achieve these results. |
| Approach: | They train a diverse set of LM architectures with and without auxiliary visual supervision on datasets of varying scales. |
| Outcome: | The proposed models exhibit better learning of syntactic categories, lexical relations, semantic features, word similarity and alignment with human neural representations. |
Dissecting Contextual Word Embeddings: Architecture and Representation (D18-1)
Copied to clipboard
| Challenge: | Existing work on learning contextual representations has used LSTM-based biLMs, but there is no reason to believe this is effective. |
| Approach: | They propose to use pre-trained bidirectional language models to learn contextual word embeddings for four NLP tasks and to use them to study the effects of architecture on endtask accuracy. |
| Outcome: | The proposed models outperform word embeddings for four NLP tasks and all learn representations that vary with network depth. |
Vokenization: Improving Language Understanding with Contextualized, Visual-Grounded Supervision (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing language pretraining frameworks only take the language context as selfsupervision . current frameworks do not take grounding information from the external visual world . |
| Approach: | They propose a visually-supervised language model that extrapolates multimodal alignments to language-only data by contextually mapping language tokens to related images. |
| Outcome: | The proposed model improves on multiple pure-language tasks. |
What do Models Learn From Training on More Than Text? Measuring Visual Commonsense Knowledge (2022.acl-srw)
Copied to clipboard
| Challenge: | Existing evaluation methods to measure what language models learn from multimodal training are lacking. |
| Approach: | They propose two evaluation tasks to measure commonsense knowledge in language models by using visual data to evaluate multimodal models and unimodal baselines. |
| Outcome: | The proposed evaluation tasks show that training on a visual modality improves on the visual commonsense knowledge in language models. |
Does Vision Accelerate Hierarchical Generalization in Neural Language Learners? (2025.coling-main)
Copied to clipboard
| Challenge: | Neural language models (LMs) are arguably less data-efficient than humans from a language acquisition perspective. |
| Approach: | They investigate the advantage of grounded language acquisition over visual input to improve syntactic generalization. |
| Outcome: | The proposed model is less efficient than humans in language acquisition . it shows that visual input helps syntactic generalization, but not vision . |
On Efficient Language and Vision Assistants for Visually-Situated Natural Language Understanding: What Matters in Reading and Reasoning (2024.emnlp-main)
Copied to clipboard
| Challenge: | Recent advances in language and vision assistants have showcased impressive capabilities but suffer from a lack of transparency, limiting broader research and reproducibility. |
| Approach: | They propose to redefine the design of vision-language models by identifying key components and creating efficient models with constrained inference costs. |
| Outcome: | The proposed models achieve significant improvements in inference throughput while maintaining high performance. |
Word Acquisition in Neural Language Models (2022.tacl-1)
Copied to clipboard
| Challenge: | Language models acquire individual words during training, based on unigram token frequencies, before transitioning loosely to bigram probabilities, eventually converging on more nuanced predictions. |
| Approach: | They examine how neural language models acquire individual words during training, extracting learning curves and ages of acquisition for over 600 words on the MacArthur-Bates Communicative Development Inventory. |
| Outcome: | The models follow consistent patterns during training for both unidirectional and bidirectional models, and for both LSTM and Transformer architectures. |
Learning to Discover, Ground and Use Words with Segmental Neural Language Models (P19-1)
Copied to clipboard
| Challenge: | Existing models of word learning do not account for the long-range dependencies manifest in language and that are easily captured by recurrent neural networks. |
| Approach: | They propose a segmental neural language model that unifies word discovery, learning how words fit together to form sentences, and by conditioning the model on visual context, how words’ meanings ground in representations of nonlinguistic modalities. |
| Outcome: | The proposed model learns predictive distributions better than character LSTM models, discovers words competitively with nonparametric Bayesian word segmentation models, and improves on both. |
Overestimation of Syntactic Representation in Neural Language Models (2020.acl-main)
Copied to clipboard
| Challenge: | Several testing methodologies have been developed to probe models’ syntactic representations. |
| Approach: | They propose a method to determine syntactic structure by training a model on strings generated according to a template and testing its ability to distinguish between similar ones with different syntax. |
| Outcome: | The proposed method reproduces positive results with two non-syntactic baseline language models: an n-gram model and an LSTM model trained on scrambled inputs. |
Improving Neural Language Models by Segmenting, Attending, and Predicting the Future (P19-1)
Copied to clipboard
| Challenge: | Common language models typically predict the next word given a past context. |
| Approach: | They propose a method that aligns the given context and the following phrase . they define syntactic heights and phrase segmentation rules to enable it to learn . |
| Outcome: | The proposed model outperforms strong baseline models on Wikitext-103 dataset. |