Search Results for author:
Found 15 papers, 8 papers with code
Self-Judge: Selective Instruction Following with Alignment Self-Evaluation
To enhance the reliability of LLMs in following instructions, we propose the study of selective instruction following, whereby the system declines to execute instructions if the anticipated response quality is low.
Preference-Guided Reflective Sampling for Aligning Language Models
It employs a tree-based generation framework to enable an efficient sampling process, which guides the direction of generation through preference and better explores the sampling space with adaptive self-refinement.
On the Robustness of Question Rewriting Systems to Questions of Varying Hardness
In conversational question answering (CQA), the task of question rewriting~(QR) in context aims to rewrite a context-dependent question into an equivalent self-contained question that gives the same answer.
Multi-Source Test-Time Adaptation as Dueling Bandits for Extractive Question Answering
In this work, we study multi-source test-time model adaptation from user feedback, where K distinct models are established for adaptation.
Test-Time Adaptation with Perturbation Consistency Learning
Accordingly, in this paper, we propose perturbation consistency learning (PCL), a simple test-time adaptation method to promote the model to make stable predictions for samples with distribution shifts.
Robust Question Answering against Distribution Shifts with Test-Time Adaptation: An Empirical Study
To answer this question, we evaluate test-time adaptation (TTA) to improve a model after deployment.
On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation
It works by adding light-weight adapter modules to a pretrained language model (PrLM) and only updating the parameters of adapter modules when learning on a downstream task.
Unsupervised Domain Adaptation of a Pretrained Cross-Lingual Language Model
Experimental results show that our proposed method achieves significant performance improvements over the state-of-the-art pretrained cross-lingual language model in the CLCD setting.
Feature Adaptation of Pre-Trained Language Models across Languages and Domains with Robust Self-Training
To improve the robustness of self-training, in this paper we present class-aware feature self-distillation (CFd) to learn discriminative features from PrLMs, in which PrLM features are self-distilled into a feature adaptation module and the features from the same class are more tightly clustered.
Deep Ranking Based Cost-sensitive Multi-label Learning for Distant Supervision Relation Extraction
Furthermore, to deal with the problem of class imbalance in distant supervision relation extraction, we further adopt cost-sensitive learning to rescale the costs from the positive and negative labels.
Jointly Learning Semantic Parser and Natural Language Generator via Dual Information Maximization
Semantic parsing aims to transform natural language (NL) utterances into formal meaning representations (MRs), whereas an NL generator achieves the reverse: producing a NL description for some given MRs.
Semi-Supervised Learning for Neural Keyphrase Generation
We study the problem of generating keyphrases that summarize the key points for a given document.
Interpretable Rationale Augmented Charge Prediction System
This paper proposes a neural based system to solve the essential interpretability problem existing in text classification, especially in charge prediction task.
Interpretable Charge Predictions for Criminal Cases: Learning to Generate Court Views from Fact Descriptions
In this paper, we propose to study the problem of COURT VIEW GENeration from the fact description in a criminal case.
Jointly Extracting Relations with Class Ties via Effective Deep Ranking
Exploiting class ties between relations of one entity tuple will be promising for distantly supervised relation extraction.
