Talks and presentations

Quark: Controllable Text Generation with Reinforced [Un]learning

April 09, 2024

RL Group Meeting, Hsinchu city, National Yang Ming Chiao Tung University, Hsinchu city, Taiwan

Large language models may generate content that is misaligned with the user’s expectations. For example, generating toxic words, repeated content, and undesired responses for users.

A Statistical Perspective on Retrieval-Based Models

October 12, 2023

Lab Seminar, Hsinchu city, National Yang Ming Chiao Tung University, Hsinchu city, Taiwan

This paper uses a formal treatment of retrieval-based models to characterize their performance via a novel statistical perspective.

A Neural Corpus Indexer for Document Retrieval

August 29, 2023

RL Group Meeting, Hsinchu city, National Yang Ming Chiao Tung University, Hsinchu city, Taiwan

Current SOTA for document retrieval solutions mainly follow an index-retrieve, where the index is hard to be directly optimized for the final retrieval target.

AdaMix: Mixture-of-Adaptations for Parameter-efficient Model Tuning

July 27, 2023

Lab Seminar, Hsinchu city, National Yang Ming Chiao Tung University, Hsinchu city, Taiwan

This paper proposes a parameter-efficient fine-tuning method called $\texttt{AdaMix}$, a general parameter-efficient fine-tuning (PEFT) techniques that tunes a mixture of adaptation modules.

Active Retrieval Augmented Generation

June 27, 2023

RL Group Meeting, Hsinchu city, National Yang Ming Chiao Tung University, Hsinchu city, Taiwan

Most existing retrieval-augmented LMs employ a retrieve-and-generate setup that only retrieves information once based on the input.

Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

May 30, 2023

RL Group Meeting, Hsinchu city, National Yang Ming Chiao Tung University, Hsinchu city, Taiwan

This study proposes a fine-tuning recipe for retrieval-augmented generation (RAG) models that combine pre-trained parametric and non-parametric memory for language generation.

Evaluating Parameter Efficient Learning for Generation

May 09, 2023

NLG Group Meeting, Hsinchu city, National Yang Ming Chiao Tung University, Hsinchu city, Taiwan

In this paper, they present a comprehensive evaluation of parameter efficient learning methods (PERMs) for generation tasks in natural language processing.

Off-Policy Deep Reinforcement Learning without Exploration

May 08, 2023

Course paper presentation, Hsinchu city, National Yang Ming Chiao Tung University, Hsinchu city, Taiwan

This paper proposes a new algorithm for off-policy reinforcement learning that combines state-of-the-art deep Q-learning algorithms with a state-conditioned generative model for producing only previously seen actions.

BEIT:BERT Pre-training of Image Transformers

December 05, 2022

Course paper presentation, Hsinchu city, National Yang Ming Chiao Tung University, Hsinchu city, Taiwan

Motivated by BERT, they turn to the denoising auto-encoding idea to pretrain vision transformers, which has not been well studied by the vision community.

Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables

December 01, 2022

Lab Seminar, Hsinchu city, National Yang Ming Chiao Tung University, Hsinchu city, Taiwan

Previous methods rely heavily on on-policy experience, limiting their sample efficiency.

They also lack mechanisms to reason about task uncertainty when adapting to new tasks, limiting their effectiveness in sparse reward problems.

This paper developing an off-policy meta-RL algorithm that disentangles task inference and control.

  1. Achieving excellent sample efficiency during meta-training, enables fast adaptation by accumulating experience online
  2. Performing structured exploration by reasoning about uncertainty over tasks

Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems

November 01, 2022

RL Group Meeting, Hsinchu city, National Yang Ming Chiao Tung University, Hsinchu city, Taiwan

This paper provide the reader with the conceptual tools needed to get started on research on offline reinforcement learning algorithms:
reinforcement learning algorithms that utilize previously collected data, without additional online data collection.

Addressing Function Approximation Error in Actor-Critic Methods

September 20, 2022

RL Group Meeting, Hsinchu City, National Yang Ming Chiao Tung University, Hsinchu city, Taiwan

In value-based reinforcement learning methods, function approximation errors are known to lead to overestimated value estimates and sub-optimal policies.