| Challenge: | Existing models that encode rich semantic and syntactic content are biased, but they are effective at encoding symbolic representations. |
| Approach: | They propose a neural language model that enforces explicit relational structures which allow for compositionality onto the output representations of pretrained language models. |
| Outcome: | The proposed model can encode sentences into sequences of symbols and infer the posterior distribution of the model from natural language datasets. |
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| Challenge: | Existing methods to construct knowledge graphs are limited to a small set of relations due to manual cost or restrictions in text corpus. |
| Approach: | They propose to automatically construct knowledge graphs (KGs) of diverse new relations from pretrained language models that accept knowledge queries with prompts. |
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Infusing Finetuning with Semantic Dependencies (2021.tacl-1)
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| Challenge: | Several diagnostics help to localize the benefits of our approach. |
| Approach: | They apply convolutional graph encoders to integrate semantic parses into task-specific finetuning. |
| Outcome: | The proposed approach yields benefits to natural language understanding (NLU) tasks in the GLUE benchmark. |
Linguistic Frameworks Go Toe-to-Toe at Neuro-Symbolic Language Modeling (2022.naacl-main)
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| Challenge: | Existing models of language understanding are based on explicit representations of hierarchical structure, but there are good reasons to doubt that they can be said to understand language in any meaningful way. |
| Approach: | They examine whether syntactic and semantic graph representations can complement and improve neural language modeling. |
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Pre-trained language model representations for language generation (N19-1)
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| Challenge: | Pre-trained language model representations have been successful in a wide range of language understanding tasks. |
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Modeling Content and Context with Deep Relational Learning (2021.tacl-1)
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| Challenge: | Existing frameworks for combining neural and symbolic representations are limited to simple relational learning tasks. |
| Approach: | They propose a declarative framework for specifying deep relational models that integrates expressive language encoders and provides an interface to study the interactions between representation, inference and learning. |
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NeuroLogic Decoding: (Un)supervised Neural Text Generation with Predicate Logic Constraints (2021.naacl-main)
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| Challenge: | Conditional text generation often requires lexical constraints, i.e., which words should or shouldn't be included in the output text. |
| Approach: | They propose an algorithm that enables neural language models to generate fluent text while satisfying complex lexical constraints. |
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What do Large Language Models Learn beyond Language? (2022.findings-emnlp)
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| Challenge: | Pretraining on text confers models with useful ‘inductive biases’ for non-linguistic reasoning. |
| Approach: | They investigate whether pre-training on text confers these models with helpful ‘inductive biases’ for non-linguistic reasoning. |
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PretrainRL: Alleviating Factuality Hallucination of Large Language Models at the Beginning (2026.findings-acl)
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Langming Liu, Kangtao Lv, Haibin Chen, Weidong Zhang, Yejing Wang, Shilei Liu, Xin Tong, Yujin Yuan, Yongwei Wang, Wenbo Su, Bo Zheng
| Challenge: | Large language models suffer from factual hallucinations where they generate verifiable falsehoods. |
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Exploring Graph Representations of Logical Forms for Language Modeling (2025.findings-acl)
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| Challenge: | Graph-based formal-logical distributional semantics models are more data-efficient than textual counterparts. |
| Approach: | They propose a pretrained language model over graph representations of logical forms as a proof-of-concept. |
| Outcome: | The proposed model outperforms textual, transformer LMs on downstream tasks . the model is likely to scale with additional parameters and pretraining data . |
Can LLMs Facilitate Interpretation of Pre-trained Language Models? (2023.emnlp-main)
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| Challenge: | Existing methods to uncover knowledge encoded within pre-trained language models are limited in terms of scalability and scope of interpretation. |
| Approach: | They propose to use a large language model, ChatGPT, as an annotation tool . they demonstrate that ChatGPt produces accurate and semantically richer annotations . |
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