| Challenge: | Existing methods for representing factual knowledge in a language model are insufficient. |
| Approach: | They propose a procedure for “crawling” the internal knowledge-base of a language model by expanding a knowledge-graph around it. |
| Outcome: | The proposed method yields high precision graphs (82-92%) while emitting a reasonable number of facts per entity. |
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| Challenge: | Pre-trained language models are used to solve tasks such as summarization and information retrieval. |
| Approach: | They propose to use word embeddings, text generators, context encoders to extract underlying knowledge graphs of nine influential language models. |
| Outcome: | The proposed model is able to encode word embeddings, text generators, and context encoders, but suffers from several inaccuracies. |
Barack’s Wife Hillary: Using Knowledge Graphs for Fact-Aware Language Modeling (P19-1)
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| Challenge: | Existing language models are only capable of remembering facts seen at training time, and have difficulty recalling them. |
| Approach: | They introduce a knowledge graph language model with mechanisms for selecting and copying facts from a Knowledge graph that are relevant to the context. |
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Estimating Knowledge in Large Language Models Without Generating a Single Token (2024.emnlp-main)
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| Challenge: | Existing methods to evaluate knowledge in large language models require querying and evaluating the model's generated responses. |
| Approach: | They ask whether it is possible to estimate how knowledgeable a model is about a subject entity only from its internal computation. |
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How Can We Know What Language Models Know? (2020.tacl-1)
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| Challenge: | Recent work examines knowledge contained in language models by having the LM fill in the blanks of prompts such as “Obama is a __ by profession”. |
| Approach: | They propose mining-based and paraphrasing-based methods to automatically generate high-quality and diverse prompts, as well as ensemble methods to combine answers from different prompts. |
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Give Me the Facts! A Survey on Factual Knowledge Probing in Pre-trained Language Models (2023.findings-emnlp)
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| Challenge: | Pre-trained language models are trained on vast unlabeled data, rich in world knowledge. |
| Approach: | They propose a categorization scheme for factual probing methods based on how inputs, outputs and probed PLMs are adapted . they synthesize insights about knowledge retention and prompt optimization in PLM models and analyze obstacles to adopting them as knowledge bases . |
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A Framework for Adapting Pre-Trained Language Models to Knowledge Graph Completion (2022.emnlp-main)
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| Challenge: | Recent work has demonstrated that entity representations can be extracted from pre-trained language models to develop knowledge graph completion models that are more robust to the naturally occurring sparsity found in knowledge graphs. |
| Approach: | They propose unsupervised and supervised methods to extract more informative representations from pre-trained language models to develop knowledge graph completion models. |
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How Large Language Models Encode Context Knowledge? A Layer-Wise Probing Study (2024.lrec-main)
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| Challenge: | Existing studies have focused on enhancing the factualness of large language models using context knowledge. |
| Approach: | They propose to use ChatGPT to construct probing datasets that provide diverse and coherent evidence corresponding to various facts. |
| Outcome: | The proposed model can encode knowledge across different layers, and it is compared with existing models. |
Scalable Construction and Reasoning of Massive Knowledge Bases (N18-6)
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| Challenge: | Existing knowledge mining systems assume abundant human annotations for training high quality machine learning models, which is impractical when trying to deploy IE systems to a broad range of domains, settings and languages. |
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Knowledge-Enriched Natural Language Generation (2021.emnlp-tutorials)
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| Challenge: | Knowledge-enriched text generation poses unique challenges in modeling and learning . a roadmap will outline the state-of-the-art methods to tackle these challenges . |
| Approach: | They propose a roadmap to tackle the challenges of knowledge-enriched text generation . they will dive deep into various technical components to illustrate how to represent knowledge . |
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A Warm Start and a Clean Crawled Corpus - A Recipe for Good Language Models (2022.lrec-1)
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Vésteinn Snæbjarnarson, Haukur Barri Símonarson, Pétur Orri Ragnarsson, Svanhvít Lilja Ingólfsdóttir, Haukur Jónsson, Vilhjalmur Thorsteinsson, Hafsteinn Einarsson
| Challenge: | Pre-trained neural language models have shown impressive results when adapted for a variety of classification and text generation tasks. |
| Approach: | They propose to use Icelandic's Icelandic Common Crawl Corpus to train language models that achieve state-of-the-art performance in downstream tasks. |
| Outcome: | The proposed models achieve state-of-the-art in a variety of downstream tasks including part-of speech tagging, named entity recognition and constituency parsing. |