| Challenge: | Existing approaches focus on adapting left-to-right language models for text infilling. |
| Approach: | They propose a model that generates sequences by dynamically creating and filling in blanks. |
| Outcome: | Experiments on style transfer and damaged ancient text restoration show that the proposed model outperforms baseline models in terms of accuracy and fluency. |
Similar Papers
Enabling Language Models to Fill in the Blanks (2020.acl-main)
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| Challenge: | Infilling is the task of predicting missing spans of text at any position in a document. |
| Approach: | They propose a framework which can be used to infill entire sentences . they train off-the-shelf LMs on sequences containing concatenation of masked text . |
| Outcome: | The proposed approach can infill entire sentences on short stories, scientific abstracts, and lyrics. |
GLM: General Language Model Pretraining with Autoregressive Blank Infilling (2022.acl-long)
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| Challenge: | Existing pretraining frameworks do not perform well for all tasks of three main categories, such as natural language understanding (NLU), unconditional generation, and conditional generation. |
| Approach: | They propose a general language model based on autoregressive blank infilling to address this challenge. |
| Outcome: | The proposed model outperforms BERT, T5, and GPT on a wide range of tasks across NLU, conditional and unconditional generation tasks. |
The Case for a Single Model that can Both Generate Continuations and Fill-in-the-Blank (2022.findings-naacl)
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| Challenge: | a natural language generation system can be used to create text at the end of a passage . fill in the blank (FITB) is a task of inserting text into a specified position in a text . |
| Approach: | They evaluate the feasibility of using a single model to perform both tasks . they show that models pre-trained with a FitB-style objective are capable of both tasks. |
| Outcome: | The proposed model can perform both fill in the blank and continuation tasks. |
Plug-in Language Model: Controlling Text Generation with a Simple Regression Model (2024.findings-naacl)
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| Challenge: | Large-scale pre-trained language models have demonstrated unrivaled capacity in generating text that closely resembles human-written content. |
| Approach: | They propose a plug-in language model that leverages reinforcement learning to adjust latent states to control text generation. |
| Outcome: | The proposed model outperforms existing methods that rely on gradient-based, weighted decoding, or prompt-based methods. |
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. |
| Approach: | They will provide a centralized discussion of critical considerations when choosing how to generate from a language model. |
| Outcome: | This tutorial will provide a centralized discussion of critical considerations when choosing how to generate from a language model. |
CDLM: Cross-Document Language Modeling (2021.findings-emnlp)
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| Challenge: | Existing language models (LMs) provide powerful representations for internal text structure, but there are important applications for multi-text tasks. |
| Approach: | They propose a pretraining approach that incorporates two key ideas into the masked language modeling objective. |
| Outcome: | The proposed model improves over existing models and sets of long-range transformers and can be easily applied to multiple multi-text tasks. |
Span Fine-tuning for Pre-trained Language Models (2021.findings-emnlp)
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| Challenge: | Existing methods to fine-tune pre-trained language models are time-consuming and lack flexibility. |
| Approach: | They propose a span fine-tuning method which allows for a more efficient and efficient way of incorporating span-level information into pre-training. |
| Outcome: | Experiments on GLUE benchmark show that the proposed method significantly enhances the PrLM and offers more flexibility in an efficient way. |
Text-Free Prosody-Aware Generative Spoken Language Modeling (2022.acl-long)
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Eugene Kharitonov, Ann Lee, Adam Polyak, Yossi Adi, Jade Copet, Kushal Lakhotia, Tu Anh Nguyen, Morgane Riviere, Abdelrahman Mohamed, Emmanuel Dupoux, Wei-Ning Hsu
| Challenge: | Experimental results show that generative spoken language models (LMs) are natural unsupervised multitask learners. |
| Approach: | They propose a prosody-aware generative spoken language model that uses discovered units to generate natural, meaningful, and coherent speech. |
| Outcome: | The proposed model can generate natural, meaningful, and coherent speech given a spoken prompt. |
Recent Advances in Pre-trained Language Models: Why Do They Work and How Do They Work (2022.aacl-tutorials)
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| Challenge: | Pre-trained language models are language models that are pre-taught on large-scaled corpora in a self-supervised fashion. |
| Approach: | This tutorial provides a broad and comprehensive introduction to pre-trained language models . it focuses on emerging methods that enable PLMs to perform diverse downstream tasks . |
| Outcome: | This tutorial focuses on the benefits of pre-trained language models and how to use them in NLP tasks. |
KidLM: Advancing Language Models for Children – Early Insights and Future Directions (2024.emnlp-main)
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| Challenge: | Large language models have been shown to be effective in creating educational tools for children, yet there are significant challenges in maintaining key child-specific properties such as linguistic nuances, cognitive needs, and safety standards. |
| Approach: | They propose a user-centric data collection pipeline that involves gathering and validating a corpus specifically written for and sometimes by children. |
| Outcome: | The proposed model excels in understanding lower grade-level text, maintains safety by avoiding stereotypes, and captures children’s unique preferences. |