Search Results for author:
Found 26 papers, 8 papers with code
Continual Few-shot Intent Detection
Intent detection is at the core of task-oriented dialogue systems.
VCBench: A Controllable Benchmark for Symbolic and Abstract Challenges in Video Cognition
Recent advancements in Large Video-Language Models (LVLMs) have driven the development of benchmarks designed to assess cognitive abilities in video-based tasks.
Optimizing Instruction Synthesis: Effective Exploration of Evolutionary Space with Tree Search
With tree search and evaluation models, it can efficiently guide each instruction to evolve into a high-quality form, aiding in instruction fine-tuning.
Role-RL: Online Long-Context Processing with Role Reinforcement Learning for Distinct LLMs in Their Optimal Roles
Large language models (LLMs) with long-context processing are still challenging because of their implementation complexity, training efficiency and data sparsity.
BeamAggR: Beam Aggregation Reasoning over Multi-source Knowledge for Multi-hop Question Answering
For composite questions, the LLM combines beam candidates, explores multiple reasoning paths through probabilistic aggregation, and prioritizes the most promising trajectory.
An Information Bottleneck Perspective for Effective Noise Filtering on Retrieval-Augmented Generation
Retrieval-augmented generation integrates the capabilities of large language models with relevant information retrieved from an extensive corpus, yet encounters challenges when confronted with real-world noisy data.
Divide-and-Conquer Meets Consensus: Unleashing the Power of Functions in Code Generation
To this end, we propose FunCoder, a code generation framework incorporating the divide-and-conquer strategy with functional consensus.
Mixed Distillation Helps Smaller Language Model Better Reasoning
While large language models (LLMs) have demonstrated exceptional performance in recent natural language processing (NLP) tasks, their deployment poses substantial challenges due to high computational and memory demands in real-world applications.
Learning to Break: Knowledge-Enhanced Reasoning in Multi-Agent Debate System
First, we involve a shared retrieval knowledge pool in the debate process to solve the problem of limited and different knowledge backgrounds.
TimeBench: A Comprehensive Evaluation of Temporal Reasoning Abilities in Large Language Models
Grasping the concept of time is a fundamental facet of human cognition, indispensable for truly comprehending the intricacies of the world.
Trends in Integration of Knowledge and Large Language Models: A Survey and Taxonomy of Methods, Benchmarks, and Applications
In this paper, we propose a review to discuss the trends in integration of knowledge and large language models, including taxonomy of methods, benchmarks, and applications.
A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions
This divergence highlights the urgency for a nuanced understanding and comprehensive overview of recent advances in LLM hallucinations.
Knowledge-enhanced Memory Model for Emotional Support Conversation
The prevalence of mental disorders has become a significant issue, leading to the increased focus on Emotional Support Conversation as an effective supplement for mental health support.
Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future
We hope this paper serves as an introduction for beginners and fosters future research.
Large Language Models Are Also Good Prototypical Commonsense Reasoners
Commonsense reasoning is a pivotal skill for large language models, yet it presents persistent challenges in specific tasks requiring this competence.
WYWEB: A NLP Evaluation Benchmark For Classical Chinese
To fully evaluate the overall performance of different NLP models in a given domain, many evaluation benchmarks are proposed, such as GLUE, SuperGLUE and CLUE.
Distinguish Before Answer: Generating Contrastive Explanation as Knowledge for Commonsense Question Answering
Existing knowledge-enhanced methods have achieved remarkable results in certain QA tasks via obtaining diverse knowledge from different knowledge bases.
AMTSS: An Adaptive Multi-Teacher Single-Student Knowledge Distillation Framework For Multilingual Language Inference
To support cost-effective language inference in multilingual settings, we propose AMTSS, an adaptive multi-teacher single-student distillation framework, which allows distilling knowledge from multiple teachers to a single student.
Task Difficulty Aware Parameter Allocation & Regularization for Lifelong Learning
In this paper, we propose the Parameter Allocation & Regularization (PAR), which adaptively select an appropriate strategy for each task from parameter allocation and regularization based on its learning difficulty.
ERNIE-mmLayout: Multi-grained MultiModal Transformer for Document Understanding
At first, a document graph is proposed to model complex relationships among multi-grained multimodal elements, in which salient visual regions are detected by a cluster-based method.
DictBERT: Dictionary Description Knowledge Enhanced Language Model Pre-training via Contrastive Learning
We evaluate our approach on a variety of knowledge driven and language understanding tasks, including NER, relation extraction, CommonsenseQA, OpenBookQA and GLUE.
Rethinking the Value of Gazetteer in Chinese Named Entity Recognition
Gazetteer is widely used in Chinese named entity recognition (NER) to enhance span boundary detection and type classification.
Semantic Sentence Composition Reasoning for Multi-Hop Question Answering
Due to the lack of insufficient data, existing multi-hop open domain question answering systems require to effectively find out relevant supporting facts according to each question.
Multi-hop Question Answering
Open-Domain Question Answering
+3
K-AID: Enhancing Pre-trained Language Models with Domain Knowledge for Question Answering
Knowledge enhanced pre-trained language models (K-PLMs) are shown to be effective for many public tasks in the literature but few of them have been successfully applied in practice.
KACE: Generating Knowledge Aware Contrastive Explanations for Natural Language Inference
In order to better understand the reason behind model behaviors (i. e., making predictions), most recent works have exploited generative models to provide complementary explanations.
Improving Commonsense Question Answering by Graph-based Iterative Retrieval over Multiple Knowledge Sources
More concretely, we first introduce a novel graph-based iterative knowledge retrieval module, which iteratively retrieves concepts and entities related to the given question and its choices from multiple knowledge sources.
