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Found 17 papers, 5 papers with code
Selective Forgetting: Advancing Machine Unlearning Techniques and Evaluation in Language Models
The aim of this study is to investigate Machine Unlearning (MU), a burgeoning field focused on addressing concerns related to neural models inadvertently retaining personal or sensitive data.
IndiVec: An Exploration of Leveraging Large Language Models for Media Bias Detection with Fine-Grained Bias Indicators
IndiVec begins by constructing a fine-grained media bias database, leveraging the robust instruction-following capabilities of large language models and vector database techniques.
A Survey of the Evolution of Language Model-Based Dialogue Systems
This survey delves into the historical trajectory of dialogue systems, elucidating their intricate relationship with advancements in language models by categorizing this evolution into four distinct stages, each marked by pivotal LM breakthroughs: 1) Early_Stage: characterized by statistical LMs, resulting in rule-based or machine-learning-driven dialogue_systems; 2) Independent development of TOD and ODD based on neural_language_models (NLM; e. g., LSTM and GRU), since NLMs lack intrinsic knowledge in their parameters; 3) fusion between different types of dialogue systems with the advert of pre-trained_language_models (PLMs), starting from the fusion between four_sub-tasks_within_TOD, and then TOD_with_ODD; and 4) current LLM-based_dialogue_system, wherein LLMs can be used to conduct TOD and ODD seamlessly.
TPE: Towards Better Compositional Reasoning over Conceptual Tools with Multi-persona Collaboration
Large language models (LLMs) have demonstrated exceptional performance in planning the use of various functional tools, such as calculators and retrievers, particularly in question-answering tasks.
KERMIT: Knowledge Graph Completion of Enhanced Relation Modeling with Inverse Transformation
Knowledge graph completion is a task that revolves around filling in missing triples based on the information available in a knowledge graph.
Delta-LoRA: Fine-Tuning High-Rank Parameters with the Delta of Low-Rank Matrices
In this paper, we present Delta-LoRA, which is a novel parameter-efficient approach to fine-tune large language models (LLMs).
A Comprehensive Survey on Deep Learning for Relation Extraction: Recent Advances and New Frontiers
This survey is expected to facilitate researchers' collaborative efforts to tackle the challenges of real-life RE systems.
KGA: A General Machine Unlearning Framework Based on Knowledge Gap Alignment
Recent legislation of the "right to be forgotten" has led to the interest in machine unlearning, where the learned models are endowed with the function to forget information about specific training instances as if they have never existed in the training set.
Strategize Before Teaching: A Conversational Tutoring System with Pedagogy Self-Distillation
Conversational tutoring systems (CTSs) aim to help students master educational material with natural language interaction in the form of a dialog.
Opportunities and Challenges in Neural Dialog Tutoring
Designing dialog tutors has been challenging as it involves modeling the diverse and complex pedagogical strategies employed by human tutors.
Improving Conversational Recommender System via Contextual and Time-Aware Modeling with Less Domain-Specific Knowledge
In addition to conducting experiments on a popular dataset (ReDial), we also include a multi-domain dataset (OpenDialKG) to show the effectiveness of our model.
Salt and pepper noise removal method based on stationary Framelet transform with non-convex sparsity regularization
For example, the noise location information is often ignored and the sparsity of the salt and pepper noise is often described by L1 norm, which cannot illustrate the sparse variables clearly.
RecInDial: A Unified Framework for Conversational Recommendation with Pretrained Language Models
Furthermore, we propose to evaluate the CRS models in an end-to-end manner, which can reflect the overall performance of the entire system rather than the performance of individual modules, compared to the separate evaluations of the two modules used in previous work.
Re-entry Prediction for Online Conversations via Self-Supervised Learning
In recent years, world business in online discussions and opinion sharing on social media is booming.
Continuity of Topic, Interaction, and Query: Learning to Quote in Online Conversations
Quotations are crucial for successful explanations and persuasions in interpersonal communications.
Quotation Recommendation and Interpretation Based on Transformation from Queries to Quotations
To help individuals express themselves better, quotation recommendation is receiving growing attention.
Coupling Global and Local Context for Unsupervised Aspect Extraction
Aspect words, indicating opinion targets, are essential in expressing and understanding human opinions.
