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Found 5 papers, 1 papers with code
Improving Multiple Documents Grounded Goal-Oriented Dialog Systems via Diverse Knowledge Enhanced Pretrained Language Model
In this paper, we mainly discuss about our submission to MultiDoc2Dial task, which aims to model the goal-oriented dialogues grounded in multiple documents.
Skeleton: A New Framework for Accelerating Language Models via Task Neuron Localized Prompt Tuning
Prompt tuning methods have shown comparable performance to general training methods as parameter-efficient fine-tuning (PEFT) methods in various natural language understanding tasks.
MP2D: An Automated Topic Shift Dialogue Generation Framework Leveraging Knowledge Graphs
Through quantitative and qualitative experiments, we demonstrate MP2D's efficacy in generating dialogue with natural topic shifts.
IterCQR: Iterative Conversational Query Reformulation with Retrieval Guidance
To address these challenges, we propose Iterative Conversational Query Reformulation (IterCQR), a methodology that conducts query reformulation without relying on human rewrites.
Task-specific Compression for Multi-task Language Models using Attribution-based Pruning
However, these language models utilize an unnecessarily large number of model parameters, even when used only for a specific task.
