Modern digital writing assistants are intelligent agents that leverage AI technologies to help users write high-quality texts. Existing AI writing assistants however typically provide only limited functions such as spelling and grammatical error checking and basic rewriting for clarity and brevity.
In the new paper Effidit: Your AI Writing Assistant, a Tencent AI Lab research team introduces Efficient and Intelligent Editing (Effidit), a digital writing assistant that leverages large-scale neural language models to provide advanced, high-quality text writing assistance in English and Chinese. Effidit significantly expands AI writing assistants’ capabilities in five categories: text completion, error checking, text polishing, keywords to sentences (K2S), and cloud input methods (cloud IME).
The Effidit online demo’s UI comprises a plain text editor for users’ raw text and a window for triggering Effidit’s core functions and displaying the final results. Users can also choose from a general or academic domain and set up a cloud IME.
The team summarizes Effidit’s five major functions as follows:
- Text completion: In this category, Effidit supports generation-based sentence completion, retrieval-based sentence completion, and phrase completion, among which only one or two functions are supported by most other writing assistants.
- Error checking: For error checking on English text, Effidit offers similar functions to other systems. For Chinese error correction, among the three error types of word substitution, insertion, and deletion, the performance of Effidit is in the first tier in correcting word substitution errors and far ahead in handling insertion and deletion errors.
- Text polishing: Three functions are supported: (context-aware) phrase polishing, sentence rewriting, and sentence expansion. In contrast, many other writing assistants often support one or two of the three functions in this category.
- K2S: The K2S module of Effidit takes one or more keywords as input and returns a list of sentences. Each output sentence either contains the input keywords or is semantically related to them. The K2S function is built for the scenario that users only have some basic concepts in their minds but do not know how to organize sentences to express their ideas.
- Cloud IME: Effidit provides a Chinese Pinyin input method as well as an English IME. Instant suggestions pop up when the user is typing in our text editor. With the help of the large-scale language models hosted on our servers, Effidit has the potential to give richer suggestions than the input methods installed on the local desktop.
The paper provides technical details for every function. For instance, in generation-based sentence completion, a simple contrastive framework (SimCTG) for neural text generation encourages the model to use discriminative and isotropic token representations and contrastive search as an efficient decoding strategy, leverage a seq2seq model for global text expansion, and more.
Demo examples of Effidit core functions and sample final results are also included in the thorough technical report.
Overall, Effidit shows impressive performance in human evaluations for coherence, fluency and informativeness. The Tencent team plans to continue improving Effidit’s system quality and make it both more helpful and easy to use.
The paper Effidit: Your AI Writing Assistant is on arXiv.
Author: Hecate He | Editor: Michael Sarazen
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Text polishing is harder than it looks. Most AI writing tools focus on generation, but the real challenge is making AI-generated drafts read naturally without losing meaning. Effidit’s approach of leveraging large language models for error checking and polish is spot-on—that’s where non-native writers struggle most. The keyword-to-sentence feature is interesting too, though I’m curious how it handles context ambiguity when the same keyword fits multiple meanings. Tools like this fill a real gap between raw AI output and publishable content.
Effidit’s combination of text completion, error correction, and sentence expansion is more practical than treating writing assistance as a single autocomplete feature. The article’s point about context-aware recommendations matters because the real value is reducing revision cycles, not merely generating more words. One limitation worth discussing is that expansion tools can quietly introduce unsupported claims, especially when users start from sparse notes. Teams should keep source checking separate from style editing and review changes with version history or tracked edits. For English polishing, I use https://aihumantextconverter.com/ to check whether tone adjustments preserve the original meaning.