Search Results for author:
Found 17 papers, 8 papers with code
Mitigating Catastrophic Forgetting in Multi-domain Chinese Spelling Correction by Multi-stage Knowledge Transfer Framework
Chinese Spelling Correction (CSC) aims to detect and correct spelling errors in given sentences.
Rethinking the Roles of Large Language Models in Chinese Grammatical Error Correction
To promote the CGEC field to better adapt to the era of LLMs, we rethink the roles of LLMs in the CGEC task so that they can be better utilized and explored in CGEC.
When LLMs Meet Cunning Questions: A Fallacy Understanding Benchmark for Large Language Models
In this paper, we challenge the reasoning and understanding abilities of LLMs by proposing a FaLlacy Understanding Benchmark (FLUB) containing cunning questions that are easy for humans to understand but difficult for models to grasp.
DeepSeek LLM: Scaling Open-Source Language Models with Longtermism
The rapid development of open-source large language models (LLMs) has been truly remarkable.
EcomGPT-CT: Continual Pre-training of E-commerce Large Language Models with Semi-structured Data
Experimental results demonstrate the effectiveness of continual pre-training of E-commerce LLMs and the efficacy of our devised data mixing strategy.
LatEval: An Interactive LLMs Evaluation Benchmark with Incomplete Information from Lateral Thinking Puzzles
With the continuous evolution and refinement of LLMs, they are endowed with impressive logical reasoning or vertical thinking capabilities.
EcomGPT: Instruction-tuning Large Language Models with Chain-of-Task Tasks for E-commerce
EcomInstruct scales up the data size and task diversity by constructing atomic tasks with E-commerce basic data types, such as product information, user reviews.
On the (In)Effectiveness of Large Language Models for Chinese Text Correction
Recently, the development and progress of Large Language Models (LLMs) have amazed the entire Artificial Intelligence community.
Correct Like Humans: Progressive Learning Framework for Chinese Text Error Correction
During the training process, ProTEC guides the model to learn text error correction by incorporating these sub-tasks into a progressive paradigm.
CLEME: Debiasing Multi-reference Evaluation for Grammatical Error Correction
Evaluating the performance of Grammatical Error Correction (GEC) systems is a challenging task due to its subjectivity.
From Retrieval to Generation: Efficient and Effective Entity Set Expansion
For efficiency, expansion time consumed by GenExpan is independent of entity vocabulary and corpus size, and GenExpan achieves an average 600% speedup compared to strong baselines.
Towards Attribute-Entangled Controllable Text Generation: A Pilot Study of Blessing Generation
Facing this dilemma, we focus on a novel CTG scenario, i. e., blessing generation which is challenging because high-quality blessing texts require CTG models to comprehensively consider the entanglement between multiple attributes (e. g., objects and occasions).
Focus Is What You Need For Chinese Grammatical Error Correction
Chinese Grammatical Error Correction (CGEC) aims to automatically detect and correct grammatical errors contained in Chinese text.
Learning from the Dictionary: Heterogeneous Knowledge Guided Fine-tuning for Chinese Spell Checking
Chinese Spell Checking (CSC) aims to detect and correct Chinese spelling errors.
Linguistic Rules-Based Corpus Generation for Native Chinese Grammatical Error Correction
Extensive experiments and detailed analyses not only demonstrate that the training data constructed by our method effectively improves the performance of CGEC models, but also reflect that our benchmark is an excellent resource for further development of the CGEC field.
Contextual Similarity is More Valuable than Character Similarity: An Empirical Study for Chinese Spell Checking
Chinese Spell Checking (CSC) task aims to detect and correct Chinese spelling errors.
Equality before the Law: Legal Judgment Consistency Analysis for Fairness
We propose to simulate judges from different groups with legal judgment prediction (LJP) models and measure the judicial inconsistency with the disagreement of the judgment results given by LJP models trained on different groups.
