Director: Generator-Classifiers For Supervised Language Modeling (2022.aacl-main)
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| Challenge: | Current language models achieve low perplexity but their resulting generations still suffer from toxic responses, repetitiveness, and contradictions. |
| Approach: | They propose a new language model architecture that uses a language modeling and a classification head for each output token. |
| Outcome: | The proposed model outperforms existing model guiding approaches in terms of accuracy and efficiency. |
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| Challenge: | Semi-supervised learning and multilingual pretraining have been shown to be effective for task-specific labelled data shortages. |
| Approach: | They propose to combine semi-supervised deep generative models and multi-lingual pretraining to form a pipeline for document classification task. |
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A Natural Bias for Language Generation Models (2023.acl-short)
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| Challenge: | a standard probabilistic model for language generation has likely not yet learnt many semantic or syntactic rules of natural language, making it difficult to estimate the probability distribution over next tokens. |
| Approach: | They propose to initialise bias terms in a model's final linear layer with the log-unigram distribution and use it to output the unigram frequency statistics as prior knowledge. |
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TempLM: Distilling Language Models into Template-Based Generators (2023.findings-acl)
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| Challenge: | Pretrained language models (PLMs) have greatly improved text generation, but they have also been known to produce unfaithful or inappropriate content. |
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Generation-driven Contrastive Self-training for Zero-shot Text Classification with Instruction-following LLM (2024.eacl-long)
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| Challenge: | a novel method to train a smaller model with LLMs for zero-shot text classification requires immense computational resources due to their substantial model size. |
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Generating Text from Language Models (2023.acl-tutorials)
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| Challenge: | a growing percentage of natural language processing tasks focus on the generation of text from probabilistic language models. |
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The Amazing World of Neural Language Generation (2020.emnlp-tutorials)
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| Challenge: | Recent years have seen a paradigm shift in neural text generation due to advances in deep contextual language modeling and transfer learning. |
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Multilingual Generation in Abstractive Summarization: A Comparative Study (2024.lrec-main)
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| Challenge: | Existing models for multilingual generation lack thorough analysis due to extensive linguistic diversity. |
| Approach: | They propose to classify multilingual generation methodologies into three categories based on their underlying modeling principles . they introduce an automatic metric to mitigate spurious correlations associated with language mixing . |
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The Importance of Generation Order in Language Modeling (D18-1)
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| Challenge: | Neural language models are universally autoregressive, generating sentences one token at a time from left to right. |
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Why Generate When You Can Discriminate? A Novel Technique for Text Classification using Language Models (2024.findings-eacl)
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| Challenge: | Existing methods for text classification using autoregressive language models are limited . authors propose a novel technique for text classification using autoreregressives . |
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Accelerating Multilingual Language Model for Excessively Tokenized Languages (2024.findings-acl)
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| Challenge: | Recent advances in large language models (LLMs) have shown a significant degree of multilingual proficiency on a variety of tasks in multiple languages. |
| Approach: | They propose a framework to fine-tune a language model head and fine-track it while preserving its performance. |
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