More room for language: Investigating the effect of retrieval on language models (2024.naacl-short)
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| Challenge: | Retrieval-augmented language models are a promising alternative to standard pretraining, but little attention has been put into understanding what this type of training scheme does to the underlying language model when analyzed as a standalone -separated from the overall retrieval pipeline. |
| Approach: | They propose an ‘ideal retrieval’ methodology to study these models in a fully controllable setting and propose a retrieval augmentation methodology to examine their effects. |
| Outcome: | The proposed model saves substantially less world knowledge in their weights, but is worse at comprehending global context. |
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Wang-Chiew Tan, Yuliang Li, Pedro Rodriguez, Richard James, Xi Victoria Lin, Alon Halevy, Wen-tau Yih
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| Challenge: | Retrieval-augmented large language models excel in various NLP tasks but are not always helpful when the knowledge required is absent in the model. |
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| Challenge: | Large Language Models (LLMs) are useful interfaces to factual knowledge, but their usefulness is limited by their tendency to deliver inconsistent answers to semantically equivalent questions. |
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| Challenge: | In this tutorial, we will provide a comprehensive overview of retrieval-based language models. |
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| Outcome: | This tutorial will provide a comprehensive overview of recent advances in retrieval-based language models. |
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| Challenge: | Several testing methodologies have been developed to probe models’ syntactic representations. |
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| Challenge: | Existing studies have shown that retrieval-augmented language models can perform better than larger models, but the optimal degree of overlap remains unclear. |
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Predicting Reference: What do Language Models Learn about Discourse Models? (2020.emnlp-main)
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| Challenge: | a growing literature that probes neural language models to assess their latent acquisition of grammatical knowledge has not investigated their acquisition of discourse modeling ability. |
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