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Found 22 papers, 9 papers with code
Word Sense Disambiguation with Knowledge-Enhanced and Local Self-Attention-based Extractive Sense Comprehension
Word sense disambiguation (WSD), identifying the most suitable meaning of ambiguous words in the given contexts according to a predefined sense inventory, is one of the most classical and challenging tasks in natural language processing.
SRLCG: Self-Rectified Large-Scale Code Generation with Multidimensional Chain-of-Thought and Dynamic Backtracking
Large language models (LLMs) have revolutionized code generation, significantly enhancing developer productivity.
WindowKV: Task-Adaptive Group-Wise KV Cache Window Selection for Efficient LLM Inference
With the advancements in long-context inference capabilities of large language models (LLMs), the KV cache has become one of the foundational components.
BianCang: A Traditional Chinese Medicine Large Language Model
The rise of large language models (LLMs) has driven significant progress in medical applications, including traditional Chinese medicine (TCM).
PMoL: Parameter Efficient MoE for Preference Mixing of LLM Alignment
Reinforcement Learning from Human Feedback (RLHF) has been proven to be an effective method for preference alignment of large language models (LLMs) and is widely used in the post-training process of LLMs.
Wrong-of-Thought: An Integrated Reasoning Framework with Multi-Perspective Verification and Wrong Information
To address these challenges, we propose Wrong-of-Thought (WoT), which includes two core modules: (1) Multi-Perspective Verification: A multi-perspective verification method for accurately refining the reasoning process and result, and (2) Wrong Information Utilization: Utilizing wrong information to alert LLMs and reduce the probability of LLMs making same mistakes.
CHECKWHY: Causal Fact Verification via Argument Structure
In this paper, we introduce CheckWhy, a challenging dataset tailored to a novel causal fact verification task: checking the truthfulness of the causal relation within claims through rigorous reasoning steps.
CroPrompt: Cross-task Interactive Prompting for Zero-shot Spoken Language Understanding
To solve this problem, we present the pioneering work of Cross-task Interactive Prompting (CroPrompt) for SLU, which enables the model to interactively leverage the information exchange across the correlated tasks in SLU.
Self-Evaluation of Large Language Model based on Glass-box Features
The proliferation of open-source Large Language Models (LLMs) underscores the pressing need for evaluation methods.
An empirical study of next-basket recommendations
This methodological rigor establishes a cohesive framework for the impartial evaluation of diverse NBR approaches.
Knowledge-Aware Prompt Tuning for Generalizable Vision-Language Models
Pre-trained vision-language models, e. g., CLIP, working with manually designed prompts have demonstrated great capacity of transfer learning.
Medical Question Summarization with Entity-driven Contrastive Learning
Although existing works have attempted to utilize Seq2Seq, reinforcement learning, or contrastive learning to solve the problem, two challenges remain: how to correctly capture question focus to model its semantic intention, and how to obtain reliable datasets to fairly evaluate performance.
Tri-Attention: Explicit Context-Aware Attention Mechanism for Natural Language Processing
In natural language processing (NLP), the context of a word or sentence plays an essential role.
A Systematical Evaluation for Next-Basket Recommendation Algorithms
Different studies often evaluate NBR approaches on different datasets, under different experimental settings, making it hard to fairly and effectively compare the performance of different NBR approaches.
Modeling Multi-interest News Sequence for News Recommendation
In MINS, a news encoder based on self-attention is devised on learn an informative embedding for each piece of news, and then a novel parallel interest network is devised to extract the potential multiple interests embedded in the news sequence in preparation for the subsequent next-news recommendations.
Modeling Complex Dependencies for Session-based Recommendations via Graph Neural Networks
Based on a strong assumption of adjacent dependency, any two adjacent items in a session are necessarily dependent in most GNN-based SBRs.
Aspect-driven User Preference and News Representation Learning for News Recommendation
News recommender systems are essential for helping users to efficiently and effectively find out those interesting news from a large amount of news.
Intra-Correlation Encoding for Chinese Sentence Intention Matching
Sentence intention matching is vital for natural language understanding.
Jointly Modeling Intra- and Inter-transaction Dependencies with Hierarchical Attentive Transaction Embeddings for Next-item Recommendation
Most existing TBRSs recommend next item by only modeling the intra-transaction dependency within the current transaction while ignoring inter-transaction dependency with recent transactions that may also affect the next item.
QLUT at SemEval-2017 Task 1: Semantic Textual Similarity Based on Word Embeddings
This paper reports the details of our submissions in the task 1 of SemEval 2017.
QLUT at SemEval-2017 Task 2: Word Similarity Based on Word Embedding and Knowledge Base
The techniques of our runs mainly make use of the word embeddings and the knowledge-based method.
