Search Results for author:
Found 7 papers, 1 papers with code
Persona is a Double-edged Sword: Mitigating the Negative Impact of Role-playing Prompts in Zero-shot Reasoning Tasks
Then, Jekyll \& Hyde collects two potential solutions from role-playing and neutral prompts and selects a better solution using the LLM evaluator.
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.
LongStory: Coherent, Complete and Length Controlled Long story Generation
A human author can write any length of story without losing coherence.
Mitigating Biases for Instruction-following Language Models via Bias Neurons Elimination
Furthermore, we propose a novel and practical bias mitigation method, CRISPR, to eliminate bias neurons of language models in instruction-following settings.
MVMR: A New Framework for Evaluating Faithfulness of Video Moment Retrieval against Multiple Distractors
However, the existing VMR framework evaluates video moment retrieval performance, assuming that a video is given, which may not reveal whether the models exhibit overconfidence in the falsely given video.
Multi-View Zero-Shot Open Intent Induction from Dialogues: Multi Domain Batch and Proxy Gradient Transfer
In Task Oriented Dialogue (TOD) system, detecting and inducing new intents are two main challenges to apply the system in the real world.
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.
