Challenge: Existing methods for character-level infilling relied on predicting sub-tokens, but this strategy was ineffective.
Approach: They propose a method to fill-in-the-mid with Starting and Ending character constraints that avoids predicting sub-tokens in inference.
Outcome: The proposed method surpasses existing methods and offers significant performance gains.

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Planning-Aware Code Infilling via Horizon-Length Prediction (2025.emnlp-main)

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Challenge: Current approaches to fill-in-the-middle (FIM) often fail to generate content that aligns well with the surrounding context.
Approach: They propose a training objective that teaches models to predict the number of remaining middle tokens at each step.
Outcome: The proposed training objective improves FIM performance by up to 24% on diverse benchmarks across file-level and repository-level.
Token Alignment via Character Matching for Subword Completion (2024.findings-acl)

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Challenge: Generative models struggle with prompts corresponding to partial tokens due to tokenization, where partial token is out-of-distribution during inference.
Approach: They propose a method to alleviate tokenization artifact on text completion by backtracking to the last complete tokens and aligning subsequent generations to match with the prompt.
Outcome: The proposed method shows that it improves on partial token scenarios with only a minor time increase.
Spelling-out is not Straightforward: LLMs’ Capability of Tokenization from Token to Characters (2025.findings-emnlp)

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Challenge: Large language models (LLMs) can spell out tokens character by character with high accuracy, yet struggle with more complex character-level tasks.
Approach: They examine how large language models internally represent character-level information during the spelling-out process.
Outcome: The embedding layer does not fully encode character-level information, especially beyond the first character.
BoundRL: Efficient Token-level Structured Text Segmentation through Reinforced Boundary Generation (2026.findings-acl)

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Challenge: Structured texts often contain elements beyond plain language, such as code snippets, which conventional sentence-level segmentation methods cannot handle effectively.
Approach: They propose a token-level approach that performs efficient token-based text segmentation and label prediction for long structured texts.
Outcome: The proposed approach outperforms existing models on short-shot prompts and SFT and standard RLVR models on complex LLM prompts.
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.
SubTokenTest: A Practical Benchmark for Real-World Sub-token Understanding (2026.acl-long)

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Challenge: Existing benchmarks have highlighted character-level tasks as lacking practical relevance . many real-world applications rely heavily on precise sub-token understanding .
Approach: They propose a benchmark that assesses sub-token understanding through practical tasks . they examine the impact of test-time scaling on sub-word reasoning .
Outcome: The proposed benchmark assesses sub-token understanding through practical tasks . it includes ten tasks across four domains and isolates tokenization-related failures .
Sub-Character Tokenization for Chinese Pretrained Language Models (2023.tacl-1)

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Challenge: Existing tokenization methods for Chinese PLMs treat each character as an indivisible token, but ignore the unique feature of the writing system where additional linguistic information exists below the character level.
Approach: They propose to encode Chinese characters into short sequences and construct Chinese vocabulary based on the encoded text.
Outcome: The proposed tokenizers can tokenize inputs into much shorter sequences, improving computational efficiency.
What is the best recipe for character-level encoder-only modelling? (2023.acl-long)

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Challenge: aims to benchmark recent progress in language understanding models that output contextualised representations at the character level.
Approach: They aim to find the best way to build and train character-level BERT-like models by comparing architectural innovations with pretraining objectives.
Outcome: The proposed model outperforms a token-based model on a set of evaluation tasks with a fixed training procedure.
Hit the Sweet Spot! Span-Level Ensemble for Large Language Models (2025.coling-main)

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Challenge: a recent study focused on sample-level and token-level ensembles, which hinder dynamic correction and enhancement of outputs during the generation process.
Approach: They propose a span-level ensemble method that balances real-time adjustments and accurate ensemble decisions.
Outcome: The proposed method improves performance across language generation tasks significantly.
Enhancing Character-Level Understanding in LLMs through Token Internal Structure Learning (2025.acl-long)

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Challenge: Large language models (LLMs) use tokenization methods but often obscure internal character structures within tokens.
Approach: They propose a method that improves models’ ability to capture character positions within tokens by training them on reverse character prediction tasks using the tokenizer’s vocabulary.
Outcome: Experiments show that the proposed method improves position prediction accuracy in large language models, enabling more precise identification of target characters in original text.

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