Search Results for author:
Found 27 papers, 6 papers with code
Less is More: Attention Supervision with Counterfactuals for Text Classification
We aim to leverage human and machine intelligence together for attention supervision.
Label-Efficient Training for Next Response Selection
This paper studies label augmentation for training dialogue response selection.
Language Models as Compilers: Simulating Pseudocode Execution Improves Algorithmic Reasoning in Language Models
Also, we show that compared to natural language, pseudocode can better guide the reasoning of LMs, even though they are trained to follow natural language instructions.
Pearl: A Review-driven Persona-Knowledge Grounded Conversational Recommendation Dataset
Our experimental results demonstrate that utterances in PEARL include more specific user preferences, show expertise in the target domain, and provide recommendations more relevant to the dialogue context than those in prior datasets.
Evidence-Focused Fact Summarization for Knowledge-Augmented Zero-Shot Question Answering
Recent studies have investigated utilizing Knowledge Graphs (KGs) to enhance Quesetion Answering (QA) performance of Large Language Models (LLMs), yet structured KG verbalization remains challengin.
Ever-Evolving Memory by Blending and Refining the Past
To effectively construct memory, it is crucial to seamlessly connect past and present information, while also possessing the ability to forget obstructive information.
Self-Consistent Reasoning-based Aspect-Sentiment Quad Prediction with Extract-Then-Assign Strategy
In the task of aspect sentiment quad prediction (ASQP), generative methods for predicting sentiment quads have shown promising results.
VerifiNER: Verification-augmented NER via Knowledge-grounded Reasoning with Large Language Models
The results suggest that VerifiNER can successfully verify errors from existing models as a model-agnostic approach.
COCOA: CBT-based Conversational Counseling Agent using Memory Specialized in Cognitive Distortions and Dynamic Prompt
The demand for conversational agents that provide mental health care is consistently increasing.
Can Large Language Models be Good Emotional Supporter? Mitigating Preference Bias on Emotional Support Conversation
Motivated by these, we explore the impact of the inherent preference in LLMs on providing emotional support, and consequently, we observe that exhibiting high preference for specific strategies hinders effective emotional support, aggravating its robustness in predicting the appropriate strategy.
Commonsense-augmented Memory Construction and Management in Long-term Conversations via Context-aware Persona Refinement
As the pioneer of persona expansion in multi-session settings, our framework facilitates better response generation via human-like persona refinement.
Large Language Models are Clinical Reasoners: Reasoning-Aware Diagnosis Framework with Prompt-Generated Rationales
Specifically, we address the clinical reasoning for disease diagnosis, where the LLM generates diagnostic rationales providing its insight on presented patient data and the reasoning path towards the diagnosis, namely Clinical Chain-of-Thought (Clinical CoT).
Coffee: Boost Your Code LLMs by Fixing Bugs with Feedback
Hence, the focus of our work is to leverage open-source code LLMs to generate helpful feedback with correct guidance for code editing.
RTSUM: Relation Triple-based Interpretable Summarization with Multi-level Salience Visualization
In this paper, we present RTSUM, an unsupervised summarization framework that utilizes relation triples as the basic unit for summarization.
Dialogue Chain-of-Thought Distillation for Commonsense-aware Conversational Agents
Hence, our focus is to facilitate such multi-hop reasoning over a dialogue context, namely dialogue chain-of-thought (CoT) reasoning.
CoTEVer: Chain of Thought Prompting Annotation Toolkit for Explanation Verification
To improve the correctness of the explanations, fine-tuning language models with explanation data is needed.
Evidence-empowered Transfer Learning for Alzheimer's Disease
To address this, we present evidence-empowered transfer learning for AD diagnosis.
TUTORING: Instruction-Grounded Conversational Agent for Language Learners
In this paper, we propose Tutoring bot, a generative chatbot trained on a large scale of tutor-student conversations for English-language learning.
BotsTalk: Machine-sourced Framework for Automatic Curation of Large-scale Multi-skill Dialogue Datasets
To build open-domain chatbots that are able to use diverse communicative skills, we propose a novel framework BotsTalk, where multiple agents grounded to the specific target skills participate in a conversation to automatically annotate multi-skill dialogues.
Mind the Gap! Injecting Commonsense Knowledge for Abstractive Dialogue Summarization
In this paper, we propose to leverage the unique characteristics of dialogues sharing commonsense knowledge across participants, to resolve the difficulties in summarizing them.
Ranked #2 on
Text Summarization
on DialogSum
Modularized Transfer Learning with Multiple Knowledge Graphs for Zero-shot Commonsense Reasoning
Towards this goal, we propose to mitigate the loss of knowledge from the interference among the different knowledge sources, by developing a modular variant of the knowledge aggregation as a new zero-shot commonsense reasoning framework.
Dual Task Framework for Improving Persona-grounded Dialogue Dataset
This paper introduces a simple yet effective data-centric approach for the task of improving persona-conditioned dialogue agents.
TrustAL: Trustworthy Active Learning using Knowledge Distillation
A traditional view of data acquisition is that, through iterations, knowledge from human labels and models is implicitly distilled to monotonically increase the accuracy and label consistency.
Meta-path Free Semi-supervised Learning for Heterogeneous Networks
Graph neural networks (GNNs) have been widely used in representation learning on graphs and achieved superior performance in tasks such as node classification.
Learning with Limited Data for Multilingual Reading Comprehension
This paper studies the problem of supporting question answering in a new language with limited training resources.
Soft Representation Learning for Sparse Transfer
Transfer learning is effective for improving the performance of tasks that are related, and Multi-task learning (MTL) and Cross-lingual learning (CLL) are important instances.
