Search Results for author:
Found 26 papers, 17 papers with code
MAIR: A Massive Benchmark for Evaluating Instructed Retrieval
Recent information retrieval (IR) models are pre-trained and instruction-tuned on massive datasets and tasks, enabling them to perform well on a wide range of tasks and potentially generalize to unseen tasks with instructions.
Parenting: Optimizing Knowledge Selection of Retrieval-Augmented Language Models with Parameter Decoupling and Tailored Tuning
Retrieval-Augmented Generation (RAG) offers an effective solution to the issues faced by Large Language Models (LLMs) in hallucination generation and knowledge obsolescence by incorporating externally retrieved knowledge.
JAILJUDGE: A Comprehensive Jailbreak Judge Benchmark with Multi-Agent Enhanced Explanation Evaluation Framework
Despite advancements in enhancing LLM safety against jailbreak attacks, evaluating LLM defenses remains a challenge, with current methods often lacking explainability and generalization to complex scenarios, leading to incomplete assessments (e. g., direct judgment without reasoning, low F1 score of GPT-4 in complex cases, bias in multilingual scenarios).
Understanding the Collapse of LLMs in Model Editing
Despite significant progress in model editing methods, their application in real-world scenarios remains challenging as they often cause large language models (LLMs) to collapse.
3AM: An Ambiguity-Aware Multi-Modal Machine Translation Dataset
Experimental results show that MMT models trained on our dataset exhibit a greater ability to exploit visual information than those trained on other MMT datasets.
LoRA Dropout as a Sparsity Regularizer for Overfitting Control
We then demonstrate the theoretical mechanism of our LoRA Dropout mechanism from the perspective of sparsity regularization by providing a generalization error bound under this framework.
Parameter Efficient Quasi-Orthogonal Fine-Tuning via Givens Rotation
With the increasingly powerful performances and enormous scales of pretrained models, promoting parameter efficiency in fine-tuning has become a crucial need for effective and efficient adaptation to various downstream tasks.
The Butterfly Effect of Model Editing: Few Edits Can Trigger Large Language Models Collapse
In this work, we reveal a critical phenomenon: even a single edit can trigger model collapse, manifesting as significant performance degradation in various benchmark tasks.
Clustering Pseudo Language Family in Multilingual Translation Models with Fisher Information Matrix
In multilingual translation research, the comprehension and utilization of language families are of paramount importance.
Instruction Distillation Makes Large Language Models Efficient Zero-shot Rankers
Furthermore, our approach surpasses the performance of existing supervised methods like monoT5 and is on par with the state-of-the-art zero-shot methods.
Domain-invariant Clinical Representation Learning by Bridging Data Distribution Shift across EMR Datasets
Due to the limited information about emerging diseases, symptoms are hard to be noticed and recognized, so that the window for clinical intervention could be ignored.
Pre-training with Aspect-Content Text Mutual Prediction for Multi-Aspect Dense Retrieval
Extensive experiments on two real-world datasets (product and mini-program search) show that our approach can outperform competitive baselines both treating aspect values as classes and conducting the same MLM for aspect and content strings.
Fused Gromov-Wasserstein Graph Mixup for Graph-level Classifications
To solve this problem, we propose a novel graph mixup algorithm called FGWMixup, which seeks a midpoint of source graphs in the Fused Gromov-Wasserstein (FGW) metric space.
Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents
In this paper, we first investigate generative LLMs such as ChatGPT and GPT-4 for relevance ranking in IR.
Mortality Prediction with Adaptive Feature Importance Recalibration for Peritoneal Dialysis Patients: a deep-learning-based study on a real-world longitudinal follow-up dataset
Here, our objective is to develop a deep learning model for a real-time, individualized, and interpretable mortality prediction model - AICare.
A Contrastive Pre-training Approach to Learn Discriminative Autoencoder for Dense Retrieval
Empirical results show that our method can significantly outperform the state-of-the-art autoencoder-based language models and other pre-trained models for dense retrieval.
Scattered or Connected? An Optimized Parameter-efficient Tuning Approach for Information Retrieval
Unlike the promising results in NLP, we find that these methods cannot achieve comparable performance to full fine-tuning at both stages when updating less than 1\% of the original model parameters.
Pre-train a Discriminative Text Encoder for Dense Retrieval via Contrastive Span Prediction
% Therefore, in this work, we propose to drop out the decoder and introduce a novel contrastive span prediction task to pre-train the encoder alone.
MedFACT: Modeling Medical Feature Correlations in Patient Health Representation Learning via Feature Clustering
Thus, to address the issues, we expect to group up strongly correlated features and learn feature correlations in a group-wise manner to reduce the learning complexity without losing generality.
Pre-training Methods in Information Retrieval
The core of information retrieval (IR) is to identify relevant information from large-scale resources and return it as a ranked list to respond to the user's information need.
B-PROP: Bootstrapped Pre-training with Representative Words Prediction for Ad-hoc Retrieval
The basic idea of PROP is to construct the \textit{representative words prediction} (ROP) task for pre-training inspired by the query likelihood model.
A Linguistic Study on Relevance Modeling in Information Retrieval
We employ 16 linguistic tasks to probe a unified retrieval model over these three retrieval tasks to answer this question.
PROP: Pre-training with Representative Words Prediction for Ad-hoc Retrieval
Recently pre-trained language representation models such as BERT have shown great success when fine-tuned on downstream tasks including information retrieval (IR).
CovidCare: Transferring Knowledge from Existing EMR to Emerging Epidemic for Interpretable Prognosis
Due to the characteristics of COVID-19, the epidemic develops rapidly and overwhelms health service systems worldwide.
AdaCare: Explainable Clinical Health Status Representation Learning via Scale-Adaptive Feature Extraction and Recalibration
It also models the correlation between clinical features to enhance the ones which strongly indicate the health status and thus can maintain a state-of-the-art performance in terms of prediction accuracy while providing qualitative interpretability.
ConCare: Personalized Clinical Feature Embedding via Capturing the Healthcare Context
Predicting the patient's clinical outcome from the historical electronic medical records (EMR) is a fundamental research problem in medical informatics.
