Search Results for author:
Found 28 papers, 12 papers with code
Amalgamating Knowledge from Two Teachers for Task-oriented Dialogue System with Adversarial Training
Instead of adopting the classic student-teacher learning of forcing the output of a student network to exactly mimic the soft targets produced by the teacher networks, we introduce two discriminators as in generative adversarial network (GAN) to transfer knowledge from two teachers to the student.
Ranked #5 on
Task-Oriented Dialogue Systems
on KVRET
Generative Adversarial Network
Task-Oriented Dialogue Systems
UncertaintyRAG: Span-Level Uncertainty Enhanced Long-Context Modeling for Retrieval-Augmented Generation
We present UncertaintyRAG, a novel approach for long-context Retrieval-Augmented Generation (RAG) that utilizes Signal-to-Noise Ratio (SNR)-based span uncertainty to estimate similarity between text chunks.
PersonaMath: Enhancing Math Reasoning through Persona-Driven Data Augmentation
To bridge this gap, we propose a data augmentation approach and introduce PersonaMathQA, a dataset derived from MATH and GSM8K, on which we train the PersonaMath models.
Learning to Generalize Unseen Domains via Multi-Source Meta Learning for Text Classification
In this paper, we study the multi-source Domain Generalization of text classification and propose a framework to use multiple seen domains to train a model that can achieve high accuracy in an unseen domain.
Training on the Benchmark Is Not All You Need
The success of Large Language Models (LLMs) relies heavily on the huge amount of pre-training data learned in the pre-training phase.
Fine-Tuning and Deploying Large Language Models Over Edges: Issues and Approaches
Additionally, large-scale foundation models have expanded to create images, audio, videos, and multi-modal contents, further emphasizing the need for efficient deployment.
Lower Layer Matters: Alleviating Hallucination via Multi-Layer Fusion Contrastive Decoding with Truthfulness Refocused
Large Language Models (LLMs) have demonstrated exceptional performance across various natural language processing tasks, yet they occasionally tend to yield content that factually inaccurate or discordant with the expected output, a phenomenon empirically referred to as "hallucination".
AgentCourt: Simulating Court with Adversarial Evolvable Lawyer Agents
Our core goal is to enable lawyer agents to learn how to argue a case, as well as improving their overall legal skills, through courtroom process simulation.
APTNESS: Incorporating Appraisal Theory and Emotion Support Strategies for Empathetic Response Generation
By incorporating emotional support strategies, we aim to enrich the model's capabilities in both cognitive and affective empathy, leading to a more nuanced and comprehensive empathetic response.
Resource Allocation and Workload Scheduling for Large-Scale Distributed Deep Learning: A Survey
The large-scale environment with large volumes of datasets, models, and computational and communication resources raises various unique challenges for resource allocation and workload scheduling in distributed deep learning, such as scheduling complexity, resource and workload heterogeneity, and fault tolerance.
II-Bench: An Image Implication Understanding Benchmark for Multimodal Large Language Models
The rapid advancements in the development of multimodal large language models (MLLMs) have consistently led to new breakthroughs on various benchmarks.
CPsyCoun: A Report-based Multi-turn Dialogue Reconstruction and Evaluation Framework for Chinese Psychological Counseling
To bridge the gap, we propose CPsyCoun, a report-based multi-turn dialogue reconstruction and evaluation framework for Chinese psychological counseling.
CPsyExam: A Chinese Benchmark for Evaluating Psychology using Examinations
In this paper, we introduce a novel psychological benchmark, CPsyExam, constructed from questions sourced from Chinese language examinations.
CLHA: A Simple yet Effective Contrastive Learning Framework for Human Alignment
Reinforcement learning from human feedback (RLHF) is a crucial technique in aligning large language models (LLMs) with human preferences, ensuring these LLMs behave in beneficial and comprehensible ways to users.
MoZIP: A Multilingual Benchmark to Evaluate Large Language Models in Intellectual Property
In this paper, we contribute a new benchmark, the first Multilingual-oriented quiZ on Intellectual Property (MoZIP), for the evaluation of LLMs in the IP domain.
Layer-wise Regularized Dropout for Neural Language Models
To solve the inconsistency between training and inference caused by the randomness of dropout, some studies use consistency training to regularize dropout at the output layer.
E-EVAL: A Comprehensive Chinese K-12 Education Evaluation Benchmark for Large Language Models
The integration of LLMs and education is getting closer and closer, however, there is currently no benchmark for evaluating LLMs that focuses on the Chinese K-12 education domain.
Forgetting before Learning: Utilizing Parametric Arithmetic for Knowledge Updating in Large Language Models
In this paper, we propose a new paradigm for fine-tuning called F-Learning (Forgetting before Learning), which employs parametric arithmetic to facilitate the forgetting of old knowledge and learning of new knowledge.
Expression Syntax Information Bottleneck for Math Word Problems
To this end, we design an Expression Syntax Information Bottleneck method for MWP (called ESIB) based on variational information bottleneck, which extracts essential features of expression syntax tree while filtering latent-specific redundancy containing syntax-irrelevant features.
DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context Learning
Dual Queries first query LLM to obtain LLM-generated knowledge such as CoT, then query the retriever to obtain the final exemplars via both question and the knowledge.
Self-Distillation with Meta Learning for Knowledge Graph Completion
In this paper, we propose a selfdistillation framework with meta learning(MetaSD) for knowledge graph completion with dynamic pruning, which aims to learn compressed graph embeddings and tackle the longtail samples.
Ranked #4 on
Link Prediction
on FB15k-237
Self-consistent Reasoning For Solving Math Word Problems
Specifically, we firstly obtain a sub-network by pruning a roberta2tree model, for the sake to use the gap on output distribution between the original roberta2tree model and the pruned sub-network to expose spurious correlative samples.
Data Augmentation for Depression Detection Using Skeleton-Based Gait Information
Gait analysis provides a non-contact, low-cost, and efficient early screening method for depression.
Scene-adaptive Knowledge Distillation for Sequential Recommendation via Differentiable Architecture Search
To realize such a goal, we propose AdaRec, a knowledge distillation (KD) framework which compresses knowledge of a teacher model into a student model adaptively according to its recommendation scene by using differentiable Neural Architecture Search (NAS).
More than Encoder: Introducing Transformer Decoder to Upsample
The local and detailed feature from the shallower layer such as boundary and tissue texture is particularly more important in medical segmentation compared with natural image segmentation.
User-specific Adaptive Fine-tuning for Cross-domain Recommendations
Fine-tuning works as an effective transfer learning technique for this objective, which adapts the parameters of a pre-trained model from the source domain to the target domain.
Imagine, Reason and Write: Visual Storytelling with Graph Knowledge and Relational Reasoning
Finally, we integrate the imaginary concepts and relational knowledge to generate human-like story based on the original semantics of images.
Ranked #2 on
Visual Storytelling
on VIST
Interactive Key-Value Memory-augmented Attention for Image Paragraph Captioning
In this paper, we propose an Interactive key-value Memory- augmented Attention model for image Paragraph captioning (IMAP) to keep track of the attention history (salient objects coverage information) along with the update-chain of the decoder state and therefore avoid generating repetitive or incomplete image descriptions.
