Revisiting subword tokenization: A case study on affixal negation in large language models (2024.naacl-long)
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| Challenge: | Negation is central to language understanding but is not properly captured by modern NLP methods. |
| Approach: | They propose to use subword tokenization methods to detect negation in large language models . they find that models can reliably recognize negation, despite mismatches in tokenization accuracy . |
| Outcome: | The proposed models can detect negation in English using subword tokenization methods despite some mismatches in tokenization accuracy and negation detection performance. |
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| Challenge: | Language models typically tokenize raw text into sequences of subword identifiers from a predefined vocabulary. |
| Approach: | They propose to tokenize raw text into sequences of subword identifiers from a predefined vocabulary . they also investigate the challenges and their impact on large language models . |
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Unlike “Likely”, “Unlike” is Unlikely: BPE-based Segmentation hurts Morphological Derivations in LLMs (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) use subword vocabularies to process and generate text. |
| Approach: | They find that Large Language Models (LLMs) perform poorly at handling some types of affixations because subwords are marked as initial- or intra-word . |
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This is not a Dataset: A Large Negation Benchmark to Challenge Large Language Models (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) have grammatical knowledge but fail to interpret negation . a recent study shows that LLMs struggle with negative sentences . |
| Approach: | They propose to use a dataset to grasp LLMs' generalization and inference capability . they also fine-tuned models to assess whether the understanding of negation can be trained . |
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An Analysis of Negation in Natural Language Understanding Corpora (2022.acl-short)
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| Challenge: | Using annotator-generated examples, one can evaluate systems with synthetic language that is not representative of language in the wild. |
| Approach: | They analyze negation in eight popular corpora spanning six natural language understanding tasks. |
| Outcome: | The proposed corpora have few negations compared to general-purpose English and are often unimportant . state-of-the-art transformers obtain significantly worse results with instances that contain negation, especially if the negations are important. |
The Impact of Negated Text on Hallucination with Large Language Models (2025.emnlp-main)
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| Challenge: | Recent studies on hallucination in large language models (LLMs) have been actively progressing in natural language processing. |
| Approach: | They propose to examine whether LLMs can recognize contextual shifts caused by negation and still reliably distinguish hallucinations comparable to affirmative cases. |
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Words, Subwords, and Morphemes: What Really Matters in the Surprisal-Reading Time Relationship? (2023.findings-emnlp)
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| Challenge: | Existing studies using LLMs on psycholinguistic data have gone unverified . a growing body of research is using word-level prediction as a computational proxy . |
| Approach: | They compare morphological, morphologic, and BPE tokenization estimates with reading time data. |
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Where are we Still Split on Tokenization? (2024.findings-eacl)
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| Challenge: | Identifying tokens is a crucial first step for many tasks in Natural Language Processing (NLP) gold tokenization is often assumed, but some work on token-level tasks is more challenging. |
| Approach: | They propose an efficient method for tokenization with subword-based language models and evaluate it on 122 languages in 20 scripts. |
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Findings of the Association for Computational Linguistics: NAACL 2022 (2022.findings-naacl)
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| Challenge: | . - (EN) |
| Approach: | . - (EN) |
| Outcome: | . - (EN) |
Towards the Roots of the Negation Problem: A Multilingual NLI Dataset and Model Scaling Analysis (2025.findings-emnlp)
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| Challenge: | Negations are key to determining sentence meaning, making them essential for logical reasoning. |
| Approach: | They construct and publish two new textual entailment datasets in four languages with paired examples differing in negation. |
| Outcome: | The results show that increasing the model size may improve the models’ ability to handle negations. |
How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) are impressive in solving tasks, but they can quickly be outdated after deployment. |
| Approach: | They provide a review of recent advances in aligning deployed large language models with the ever-changing world knowledge. |
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