💬 NLP

Are you going to finish that? A Practical Study of the Partial Token Problem

This paper investigates the "partial token problem," demonstrating that realistic prompts ending mid-token—particularly in languages without whitespace, compounding languages, and code—cause severe probability distortions in large language models that do not improve with scale, while validating the effectiveness of exact inference-time solutions.

Original authors: Hao Xu, Alisa Liu, Jonathan Hayase, Yejin Choi, Noah A. Smith

Published 2026-02-04

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Hao Xu, Alisa Liu, Jonathan Hayase, Yejin Choi, Noah A. Smith

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine you are playing a game of "Finish the Sentence" with a very smart, but slightly rigid, robot. You type a sentence, and the robot is supposed to guess the very next word.

Usually, this works perfectly. But this paper discovers a specific glitch where the robot gets confused, not because it doesn't know the words, but because of how it counts them.

Here is the breakdown of the problem, the evidence, and the fix, using simple analogies.

1. The Core Problem: The "Half-Brick" Glitch

Think of the robot's brain as a library where every book is made of bricks (tokens).

The User's View: You see words (like "apple" or "is").
The Robot's View: It sees bricks. Sometimes, one brick covers one whole word. Sometimes, one brick covers two words. Sometimes, a brick is cut in half.

The Partial Token Problem happens when you stop typing right in the middle of one of these bricks.

The Analogy: Imagine the robot expects the word "processing" to be one single, solid brick.
The Mistake: You type "process" and stop.
The Confusion: The robot sees "process" and thinks, "Wait, I know the brick 'process'. But I also know the brick 'processing'. If I just add the letter 'ing' next, I'm breaking my own rules because 'processing' is supposed to be one unbreakable brick!"

Because the robot was trained to only see the whole brick, it gets terrified of completing the word the way you expect. It thinks, "If you stopped at 'process', you probably don't want 'ing' next; you probably want something totally different."

2. Where Does This Happen? (The Three Traps)

The paper found that this isn't just a rare English glitch. It happens often in three specific situations where "words" and "bricks" don't line up:

Languages without spaces (like Chinese): In English, spaces tell the robot where a word ends. In Chinese, there are no spaces. The robot has to guess where one word ends and the next begins.
- Example: The phrase "is a" might be one single brick. If you type "is" and stop, you've cut that brick in half. The robot panics.
Languages that glue words together (like German): German often combines two words into one giant word (like "Eigelb" for egg yolk).
- Example: If the robot sees "Ei" (egg) and you stop there, but the robot thinks "Eigelb" is one brick, it gets confused about what comes next.
Computer Code: Code uses symbols like () or :. Often, a whole chunk of symbols is one brick.
- Example: If you type def main() and stop, but the robot thinks (): is one single brick, it gets stuck trying to guess the next symbol.

3. How Bad Is It? (The "Silent Crash")

The researchers tested this with many different robots (AI models). The results were shocking:

The Probability Drop: When the robot is forced to guess after a "half-brick," it becomes 1,000 to 10,000 times less confident that it should give you the correct answer. It's like a student who knows the answer but is so scared of the teacher's grading rules that they refuse to write it down.
The Accuracy Drop: The robot stops giving the right answer 60% to 95% of the time. It might skip a letter, add a weird space, or give a completely wrong word.
Bigger Isn't Better: You might think, "If we make the robot smarter (bigger model), it will fix this." Nope. The paper found that bigger, smarter robots actually get worse at this. They are so trained on their specific "brick" rules that they are even more stubborn about not breaking them.

4. The Solutions: How to Fix the Glitch

The paper tested two ways to fix this during the "thinking" phase (inference):

Method A: "Token Healing" (The Retreat):
- How it works: If you type "process" and the robot is confused, this method tells the robot, "Okay, forget the last part. Pretend you only saw 'pro'."
- The Result: It helps sometimes, but it's hit-or-miss. It's like trying to fix a broken vase by cutting off the top; it might fit, but it's not perfect.
Method B: "ByteSampler" (The Map):
- How it works: Instead of guessing, this method draws a map of every possible way the robot could have read your sentence. It finds the path that matches your text exactly and then continues from there.
- The Result: It works perfectly. In the tests, this method got the robot to give the correct answer 100% of the time, even when the prompt was cut in the middle of a brick. It's like having a GPS that recalculates the route instantly when you take a wrong turn, ensuring you still get to the destination.

The Bottom Line

The paper concludes that while AI models are amazing at understanding language, they are also slaves to the specific way they were taught to count words (tokenization). If a user stops typing in a way that doesn't match the robot's internal counting rules, the robot's confidence crashes.

The good news? We don't need to rebuild the robots. We just need to use a smarter "GPS" (like ByteSampler) while they are thinking to ensure they don't get tripped up by their own counting rules.

Technical Summary: "Are you going to finish that? A Practical Study of the Partial Token Problem"

Problem Definition
The Partial Token Problem (PTP) arises from a fundamental mismatch between how users interact with Language Models (LMs) and how LMs process text. Users provide prompts as strings of characters, while LMs operate on sequences of tokens generated by a tokenizer. The PTP occurs when a user's prompt ends in the middle of a valid token (a "partial token"). Because LMs are trained exclusively on valid token sequences, they assign unexpectedly low probabilities to continuations that complete such partial tokens. While the issue is well-documented in literature (e.g., Lundberg, 2023; Ribeiro, 2023), prior work has largely relied on arbitrary character prefixes to illustrate the phenomenon. Consequently, the prevalence and severity of PTP in realistic prompts that respect natural word or syntactic boundaries have remained underexplored.

Methodology
The authors systematically investigate the PTP in realistic scenarios by identifying domains where token boundaries frequently misalign with semantic or syntactic boundaries:

Logographic Systems (Chinese): Words are not delimited by whitespace, causing tokenizers to merge characters across word boundaries.
Highly Compounding Languages (German): Compound words are often segmented by tokenizers in ways that ignore morphological boundaries.
Code: Identifiers and consecutive punctuation are frequently merged into single tokens.

To quantify the issue, the authors constructed a "repeat-after-me" task. They segmented natural sentences into prefixes and continuations such that:

The prefix ends at a complete word boundary (semantically natural).
The prefix ends at a partial token boundary relative to the tokenizer (violating the condition that encode(prompt) + encode(continuation) == encode(prompt + continuation)).
The continuation is unambiguous (the model must repeat the exact provided text).

The study evaluated a diverse suite of frontier models (including QWEN3, HUNYUAN, LLAMA, OLMO, GEMMA, and MISTRAL) across these domains. Performance was measured using two metrics:

$\Delta$ Logprob: The difference in log-probability assigned to the correct next-token between the word-aligned (partial token) prompt and a token-aligned (backed-off) prompt.
$\Delta$ Acc: The difference in accuracy when greedily decoding the correct continuation.

Key Results

High Prevalence of Misalignment: Off-the-shelf tokenizers exhibit significant misalignment rates. In Chinese, 14%–25% of word boundaries do not align with token boundaries. In code, punctuation boundaries lie inside tokens at rates exceeding 50% for most models.
Severe Probability Distortion: When faced with a PTP, models assign drastically lower probabilities to the correct continuation. In Chinese, the probability drops by up to four orders of magnitude compared to token-aligned prompts.
Accuracy Collapse: Models predict the correct continuation 60%–95% less often when the prompt ends with a partial token. For example, QWEN3-32B's accuracy in Chinese dropped from 93.92% (token-aligned) to 15.36% (word-aligned).
Scaling Does Not Help: The degradation does not diminish with model scale; in many cases, larger models suffer equal or greater drops in performance. The authors hypothesize that larger models are better fitted to their specific training tokenizers, making them more confident that a sequence of tokens (e.g., process + in) cannot be followed by a character (e.g., g) that would form a single token (processing).
Inference-Time Mitigations:
- Token Healing: A heuristic approach that removes the final token(s) from the prompt and constrains the generation to match the removed text. This showed mixed results, improving accuracy in some cases but failing in others (e.g., German).
- ByteSampler: An exact solution (Hayase et al., 2025) that constructs a tree of all valid token sequences covering the prompt prefix and samples a path. This method achieved 100% accuracy across all three domains (Chinese, German, Code) with low computational overhead (0.12–1.17 additional forward passes).

Significance and Contributions
The paper demonstrates that the Partial Token Problem is not merely an artifact of arbitrary text truncation but a serious failure mode in realistic, semantically natural use cases. The authors claim that:

Real-World Impact: The problem is pervasive in non-English languages and code, affecting even prompts that end with complete words.
Limitations of Scaling: Increasing model size does not resolve the issue, as models become more rigidly bound to their training tokenization schemes.
Practical Recommendations: The study validates the effectiveness of exact inference-time solutions like ByteSampler, which can fully mitigate the problem without requiring model retraining.

The work underscores that LMs model not just language, but also the specific tokenizer they were trained with, and that this interaction creates predictable biases when prompt boundaries do not align with token boundaries. The authors provide practical recommendations for inference providers to adopt exact solutions to ensure reliability in diverse linguistic and coding contexts.