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Found 40 papers, 15 papers with code
DAST: Context-Aware Compression in LLMs via Dynamic Allocation of Soft Tokens
Large Language Models (LLMs) face computational inefficiencies and redundant processing when handling long context inputs, prompting a focus on compression techniques.
One Example Shown, Many Concepts Known! Counterexample-Driven Conceptual Reasoning in Mathematical LLMs
Inspired by the pedagogical method of "proof by counterexamples" commonly used in human mathematics education, our work aims to enhance LLMs' ability to conduct mathematical reasoning and proof through counterexamples.
Refine Knowledge of Large Language Models via Adaptive Contrastive Learning
In our work, by imitating the human learning process, we design an Adaptive Contrastive Learning strategy.
Exploring the Implicit Semantic Ability of Multimodal Large Language Models: A Pilot Study on Entity Set Expansion
The rapid development of multimodal large language models (MLLMs) has brought significant improvements to a wide range of tasks in real-world applications.
Loss-Aware Curriculum Learning for Chinese Grammatical Error Correction
Chinese grammatical error correction (CGEC) aims to detect and correct errors in the input Chinese sentences.
Benchmarking Multimodal Retrieval Augmented Generation with Dynamic VQA Dataset and Self-adaptive Planning Agent
To bridge the dataset gap, we first construct Dyn-VQA dataset, consisting of three types of "dynamic" questions, which require complex knowledge retrieval strategies variable in query, tool, and time: (1) Questions with rapidly changing answers.
ProductAgent: Benchmarking Conversational Product Search Agent with Asking Clarification Questions
To address this task, we propose ProductAgent, a conversational information seeking agent equipped with abilities of strategic clarification question generation and dynamic product retrieval.
UltraWiki: Ultra-fine-grained Entity Set Expansion with Negative Seed Entities
To solve this issue, we first introduce negative seed entities in the inputs, which belong to the same fine-grained semantic class as the positive seed entities but differ in certain attributes.
Evaluating Robustness of Generative Search Engine on Adversarial Factual Questions
To this end, we propose evaluating the robustness of generative search engines in the realistic and high-risk setting, where adversaries have only black-box system access and seek to deceive the model into returning incorrect responses.
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.
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.
When LLMs Meet Cunning Texts: 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 texts that are easy for humans to understand but difficult for models to grasp.
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.
Towards Real-World Writing Assistance: A Chinese Character Checking Benchmark with Faked and Misspelled Characters
To the best of our knowledge, Visual-C$^3$ is the first real-world visual and the largest human-crafted dataset for the Chinese character checking scenario.
MixEdit: Revisiting Data Augmentation and Beyond for Grammatical Error Correction
In this paper, we aim to clarify how data augmentation improves GEC models.
A Frustratingly Easy Plug-and-Play Detection-and-Reasoning Module for Chinese Spelling Check
In recent years, Chinese Spelling Check (CSC) has been greatly improved by designing task-specific pre-training methods or introducing auxiliary tasks, which mostly solve this task in an end-to-end fashion.
Retrieval-Augmented Meta Learning for Low-Resource Text Classification
However, due to the limited training data in the meta-learning scenario and the inherent properties of parameterized neural networks, poor generalization performance has become a pressing problem that needs to be addressed.
Prompt Learning With Knowledge Memorizing Prototypes For Generalized Few-Shot Intent Detection
Previous GFSID methods rely on the episodic learning paradigm, which makes it hard to extend to a generalized setup as they do not explicitly learn the classification of seen categories and the knowledge of seen intents.
SeqGPT: An Out-of-the-box Large Language Model for Open Domain Sequence Understanding
However, LLMs are sometimes too footloose for natural language understanding (NLU) tasks which always have restricted output and input format.
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.
Rethinking Uncertainly Missing and Ambiguous Visual Modality in Multi-Modal Entity Alignment
As a crucial extension of entity alignment (EA), multi-modal entity alignment (MMEA) aims to identify identical entities across disparate knowledge graphs (KGs) by exploiting associated visual information.
Ranked #1 on
Multi-modal Entity Alignment
on UMVM-oea-d-w-v2
(using extra training data)
MESED: A Multi-modal Entity Set Expansion Dataset with Fine-grained Semantic Classes and Hard Negative Entities
The Entity Set Expansion (ESE) task aims to expand a handful of seed entities with new entities belonging to the same semantic class.
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.
Bidirectional End-to-End Learning of Retriever-Reader Paradigm for Entity Linking
Entity Linking (EL) is a fundamental task for Information Extraction and Knowledge Graphs.
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.
Investigating Graph Structure Information for Entity Alignment with Dangling Cases
To improve the exploitation of the structural information, we propose a novel entity alignment framework called Weakly-Optimal Graph Contrastive Learning (WOGCL), which is refined on three dimensions : (i) Model.
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.
Knowledge-augmented Few-shot Visual Relation Detection
Inspired by this, we devise a knowledge-augmented, few-shot VRD framework leveraging both textual knowledge and visual relation knowledge to improve the generalization ability of few-shot VRD.
Vision, Deduction and Alignment: An Empirical Study on Multi-modal Knowledge Graph Alignment
Entity alignment (EA) for knowledge graphs (KGs) plays a critical role in knowledge engineering.
Embracing Ambiguity: Improving Similarity-oriented Tasks with Contextual Synonym Knowledge
Extensive experiments demonstrate that PICSO can dramatically outperform the original PLMs and the other knowledge and synonym injection models on four different similarity-oriented tasks.
Active Relation Discovery: Towards General and Label-aware Open Relation Extraction
Open Relation Extraction (OpenRE) aims to discover novel relations from open domains.
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).
A Curriculum Learning Approach for Multi-domain Text Classification Using Keyword weight Ranking
We noted that the distinctness of the domain-specific features is different, so in this paper, we propose to use a curriculum learning strategy based on keyword weight ranking to improve the performance of multi-domain text classification models.
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.
Automatic Context Pattern Generation for Entity Set Expansion
In addition, we propose the GAPA, a novel ESE framework that leverages the aforementioned GenerAted PAtterns to expand target entities.
Global Mixup: Eliminating Ambiguity with Clustering
(2) linear combination significantly limits the sampling space for generating samples.
Contrastive Learning with Hard Negative Entities for Entity Set Expansion
In addition, we propose the ProbExpan, a novel probabilistic ESE framework utilizing the entity representation obtained by the aforementioned language model to expand entities.
The Past Mistake is the Future Wisdom: Error-driven Contrastive Probability Optimization for Chinese Spell Checking
However, there exists a gap between the learned knowledge of PLMs and the goal of CSC task.
Learning Purified Feature Representations from Task-irrelevant Labels
Learning an empirically effective model with generalization using limited data is a challenging task for deep neural networks.
