Enhancing Character-Level Understanding in LLMs through Token Internal Structure Learning
- URL: http://arxiv.org/abs/2411.17679v3
- Date: Tue, 17 Dec 2024 12:37:47 GMT
- Title: Enhancing Character-Level Understanding in LLMs through Token Internal Structure Learning
- Authors: Zhu Xu, Zhiqiang Zhao, Zihan Zhang, Yuchi Liu, Quanwei Shen, Fei Liu, Yu Kuang, Jian He, Conglin Liu,
- Abstract summary: Token Internal Position Awareness (TIPA) is a method that significantly improves models' ability to capture character positions within tokens.
TIPA enhances position prediction accuracy in large language models, enabling more precise identification of target characters in original text.
- Score: 20.801571525710834
- License:
- Abstract: Tokenization methods like Byte-Pair Encoding (BPE) enhance computational efficiency in large language models (LLMs) but often obscure internal character structures within tokens. This limitation hinders LLMs' ability to predict precise character positions, which is crucial in tasks like Chinese Spelling Correction (CSC) where identifying the positions of misspelled characters accelerates correction processes. We propose Token Internal Position Awareness (TIPA), a method that significantly improves models' ability to capture character positions within tokens by training them on reverse character prediction tasks using the tokenizer's vocabulary. Experiments demonstrate that TIPA enhances position prediction accuracy in LLMs, enabling more precise identification of target characters in original text. Furthermore, when applied to downstream tasks that do not require exact position prediction, TIPA still boosts performance in tasks needing character-level information, validating its versatility and effectiveness.
Related papers
- Enhancing LLM Character-Level Manipulation via Divide and Conquer [108.6908427615402]
Large Language Models (LLMs) have demonstrated strong generalization capabilities across a wide range of natural language processing (NLP) tasks.
They exhibit notable weaknesses in character-level string manipulation, struggling with fundamental operations such as character deletion, insertion, and substitution.
We propose Character-Level Manipulation via Divide and Conquer, a novel approach designed to bridge the gap between token-level processing and character-level manipulation.
arXiv Detail & Related papers (2025-02-12T07:37:39Z) - C-LLM: Learn to Check Chinese Spelling Errors Character by Character [61.53865964535705]
We propose C-LLM, a Large Language Model-based Chinese Spell Checking method that learns to check errors Character by Character.
C-LLM achieves an average improvement of 10% over existing methods.
arXiv Detail & Related papers (2024-06-24T11:16:31Z) - Contextual Position Encoding: Learning to Count What's Important [42.038277620194]
We propose a new position encoding method, Contextual Position Flop (CoPE)
CoPE allows positions to be conditioned on context by incrementing position on certain tokens determined by the model.
We show that CoPE can solve the selective copy, counting and Flip-Flop tasks where popular position embeddings fail.
arXiv Detail & Related papers (2024-05-29T02:57:15Z) - SEP: Self-Enhanced Prompt Tuning for Visual-Language Model [93.94454894142413]
We introduce a novel approach named Self-Enhanced Prompt Tuning (SEP)
SEP explicitly incorporates discriminative prior knowledge to enhance both textual-level and visual-level embeddings.
Comprehensive evaluations across various benchmarks and tasks confirm SEP's efficacy in prompt tuning.
arXiv Detail & Related papers (2024-05-24T13:35:56Z) - Identifying and Analyzing Task-Encoding Tokens in Large Language Models [55.03191279766383]
In this paper, we identify and analyze task-encoding tokens on whose representations the task performance depends.
We show that template and stopword tokens are the most prone to be task-encoding.
Our work sheds light on how large language models (LLMs) learn to perform a task from demonstrations, deepens our understanding of the varied roles different types of tokens play in LLMs, and provides insights for avoiding instability from improperly utilizing task-encoding tokens.
arXiv Detail & Related papers (2024-01-20T20:55:21Z) - Reducing Sequence Length by Predicting Edit Operations with Large
Language Models [50.66922361766939]
This paper proposes predicting edit spans for the source text for local sequence transduction tasks.
We apply instruction tuning for Large Language Models on the supervision data of edit spans.
Experiments show that the proposed method achieves comparable performance to the baseline in four tasks.
arXiv Detail & Related papers (2023-05-19T17:51:05Z) - Memory Augmented Lookup Dictionary based Language Modeling for Automatic
Speech Recognition [20.926163659469587]
We propose a new memory augmented lookup dictionary based Transformer architecture for LM.
The newly introduced lookup dictionary incorporates rich contextual information in training set, which is vital to correctly predict long-tail tokens.
Our proposed method is proved to outperform the baseline Transformer LM by a great margin on both word/character error rate and tail tokens error rate.
arXiv Detail & Related papers (2022-12-30T22:26:57Z) - Word Order Matters when you Increase Masking [70.29624135819884]
We study the effect of removing position encodings on the pre-training objective itself, to test whether models can reconstruct position information from co-occurrences alone.
We find that the necessity of position information increases with the amount of masking, and that masked language models without position encodings are not able to reconstruct this information on the task.
arXiv Detail & Related papers (2022-11-08T18:14:04Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.