Search Results for author:
Found 10 papers, 6 papers with code
RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Horizon Generation
We explore how iterative revising a chain of thoughts with the help of information retrieval significantly improves large language models' reasoning and generation ability in long-horizon generation tasks, while hugely mitigating hallucination.
Selecting Large Language Model to Fine-tune via Rectified Scaling Law
The ever-growing ecosystem of LLMs has posed a challenge in selecting the most appropriate pre-trained model to fine-tune amidst a sea of options.
JARVIS-1: Open-World Multi-task Agents with Memory-Augmented Multimodal Language Models
Achieving human-like planning and control with multimodal observations in an open world is a key milestone for more functional generalist agents.
MCU: A Task-centric Framework for Open-ended Agent Evaluation in Minecraft
To pursue the goal of creating an open-ended agent in Minecraft, an open-ended game environment with unlimited possibilities, this paper introduces a task-centric framework named MCU for Minecraft agent evaluation.
FLatS: Principled Out-of-Distribution Detection with Feature-Based Likelihood Ratio Score
Backed by theoretical analysis, this paper advocates for the measurement of the "OOD-ness" of a test case $\boldsymbol{x}$ through the likelihood ratio between out-distribution $\mathcal P_{\textit{out}}$ and in-distribution $\mathcal P_{\textit{in}}$.
Class Incremental Learning via Likelihood Ratio Based Task Prediction
An emerging theory-guided approach (called TIL+OOD) is to train a task-specific model for each task in a shared network for all tasks based on a task-incremental learning (TIL) method to deal with catastrophic forgetting.
Continual Pre-training of Language Models
A novel proxy is also proposed to preserve the general knowledge in the original LM.
Ranked #1 on
Continual Pretraining
on ACL-ARC
Adapting a Language Model While Preserving its General Knowledge
This paper shows that the existing methods are suboptimal and proposes a novel method to perform a more informed adaptation of the knowledge in the LM by (1) soft-masking the attention heads based on their importance to best preserve the general knowledge in the LM and (2) contrasting the representations of the general and the full (both general and domain knowledge) to learn an integrated representation with both general and domain-specific knowledge.
Continual Training of Language Models for Few-Shot Learning
Recent work on applying large language models (LMs) achieves impressive performance in many NLP applications.
Ranked #1 on
Continual Pretraining
on AG News
Efficient Out-of-Distribution Detection via CVAE data Generation
Recently, contrastive loss with data augmentation and pseudo class creation has been shown to produce markedly better results for out-of-distribution (OOD) detection than previous methods.
