Search Results for author:
Found 10 papers, 6 papers with code
Automatic Instruction Evolving for Large Language Models
Fine-tuning large pre-trained language models with Evol-Instruct has achieved encouraging results across a wide range of tasks.
Tapilot-Crossing: Benchmarking and Evolving LLMs Towards Interactive Data Analysis Agents
The challenges and costs of collecting realistic interactive logs for data analysis hinder the quantitative evaluation of Large Language Model (LLM) agents in this task.
A Preliminary Study of the Intrinsic Relationship between Complexity and Alignment
Training large language models (LLMs) with open-domain instruction data has yielded remarkable success in aligning to end tasks and human preferences.
Causal Document-Grounded Dialogue Pre-training
To tackle this issue, we are the first to present a causally-complete dataset construction strategy for building million-level DocGD pre-training corpora.
API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs
(2) How can we enhance LLMs' ability to utilize tools?
Semi-Supervised Lifelong Language Learning
In this paper, we explore a novel setting, semi-supervised lifelong language learning (SSLL), where a model learns sequentially arriving language tasks with both labeled and unlabeled data.
Hard Gate Knowledge Distillation -- Leverage Calibration for Robust and Reliable Language Model
The question is answered in our work with the concept of model calibration; we view a teacher model not only as a source of knowledge but also as a gauge to detect miscalibration of a student.
Prompt Conditioned VAE: Enhancing Generative Replay for Lifelong Learning in Task-Oriented Dialogue
Lifelong learning (LL) is vital for advanced task-oriented dialogue (ToD) systems.
Improving Meta-learning for Low-resource Text Classification and Generation via Memory Imitation
Building models of natural language processing (NLP) is challenging in low-resource scenarios where only limited data are available.
SeqPATE: Differentially Private Text Generation via Knowledge Distillation
Differentially private (DP) learning algorithms provide guarantees on identifying the existence of a training sample from model outputs.
