Search Results for author:
Found 26 papers, 21 papers with code
FD2Talk: Towards Generalized Talking Head Generation with Facial Decoupled Diffusion Model
Therefore, we propose a Facial Decoupled Diffusion model for Talking head generation called FD2Talk, which fully leverages the advantages of diffusion models and decouples the complex facial details through multi-stages.
A Practical Review of Mechanistic Interpretability for Transformer-Based Language Models
Mechanistic interpretability (MI) is an emerging sub-field of interpretability that seeks to understand a neural network model by reverse-engineering its internal computations.
An Investigation of Neuron Activation as a Unified Lens to Explain Chain-of-Thought Eliciting Arithmetic Reasoning of LLMs
Our analyses revealed that the activation of reasoning neurons in the feed-forward layers of an LLM can explain the importance of various components in a CoT prompt, and future research can extend it for a more complete understanding.
Look Further Ahead: Testing the Limits of GPT-4 in Path Planning
Large Language Models (LLMs) have shown impressive capabilities across a wide variety of tasks.
MathVC: An LLM-Simulated Multi-Character Virtual Classroom for Mathematics Education
Mathematical modeling (MM) is considered a fundamental skill for students in STEM disciplines.
Lens: A Foundation Model for Network Traffic in Cybersecurity
Network traffic refers to the amount of data being sent and received over the internet or any system that connects computers.
Can Large Language Models be Good Path Planners? A Benchmark and Investigation on Spatial-temporal Reasoning
Large language models (LLMs) have achieved remarkable success across a wide spectrum of tasks; however, they still face limitations in scenarios that demand long-term planning and spatial reasoning.
Large Language Model Cascades with Mixture of Thoughts Representations for Cost-efficient Reasoning
Large language models (LLMs) such as GPT-4 have exhibited remarkable performance in a variety of tasks, but this strong performance often comes with the high expense of using paid API services.
Instances Need More Care: Rewriting Prompts for Instances with LLMs in the Loop Yields Better Zero-Shot Performance
Large language models (LLMs) have revolutionized zero-shot task performance, mitigating the need for task-specific annotations while enhancing task generalizability.
Gentopia: A Collaborative Platform for Tool-Augmented LLMs
We present gentopia, an ALM framework enabling flexible customization of agents through simple configurations, seamlessly integrating various language models, task formats, prompting modules, and plugins into a unified paradigm.
Improving Generalization in Language Model-Based Text-to-SQL Semantic Parsing: Two Simple Semantic Boundary-Based Techniques
Compositional and domain generalization present significant challenges in semantic parsing, even for state-of-the-art semantic parsers based on pre-trained language models (LMs).
Ranked #6 on
Text-To-SQL
on spider
Learning to Simulate Natural Language Feedback for Interactive Semantic Parsing
In this work, we propose a new task of simulating NL feedback for interactive semantic parsing.
PoseRAC: Pose Saliency Transformer for Repetitive Action Counting
Moreover, we introduce a pose-level method, PoseRAC, which is based on this representation and achieves state-of-the-art performance on two new version datasets by using Pose Saliency Annotation to annotate salient poses for training.
Ranked #2 on
Repetitive Action Counting
on RepCount
(using extra training data)
Explaining Large Language Model-Based Neural Semantic Parsers (Student Abstract)
While large language models (LLMs) have demonstrated strong capability in structured prediction tasks such as semantic parsing, few amounts of research have explored the underlying mechanisms of their success.
Synthetic Question Value Estimation for Domain Adaptation of Question Answering
Synthesizing QA pairs with a question generator (QG) on the target domain has become a popular approach for domain adaptation of question answering (QA) models.
UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models
Structured knowledge grounding (SKG) leverages structured knowledge to complete user requests, such as semantic parsing over databases and question answering over knowledge bases.
Ranked #1 on
Task-Oriented Dialogue Systems
on KVRET
Code Editing from Few Exemplars by Adaptive Multi-Extent Composition
To achieve this, we propose a machine learning approach to adapt the editorial style derived from few exemplars to a query code snippet.
Learning Structural Edits via Incremental Tree Transformations
To show the unique benefits of modeling tree edits directly, we further propose a novel edit encoder for learning to represent edits, as well as an imitation learning method that allows the editor to be more robust.
CliniQG4QA: Generating Diverse Questions for Domain Adaptation of Clinical Question Answering
Clinical question answering (QA) aims to automatically answer questions from medical professionals based on clinical texts.
An Imitation Game for Learning Semantic Parsers from User Interaction
Despite the widely successful applications, bootstrapping and fine-tuning semantic parsers are still a tedious process with challenges such as costly data annotation and privacy risks.
Model-based Interactive Semantic Parsing: A Unified Framework and A Text-to-SQL Case Study
As a promising paradigm, interactive semantic parsing has shown to improve both semantic parsing accuracy and user confidence in the results.
Reinforced Dynamic Reasoning for Conversational Question Generation
This paper investigates a new task named Conversational Question Generation (CQG) which is to generate a question based on a passage and a conversation history (i. e., previous turns of question-answer pairs).
CoaCor: Code Annotation for Code Retrieval with Reinforcement Learning
In this work, we investigate a novel perspective of Code annotation for Code retrieval (hence called `CoaCor'), where a code annotation model is trained to generate a natural language annotation that can represent the semantic meaning of a given code snippet and can be leveraged by a code retrieval model to better distinguish relevant code snippets from others.
Interactive Semantic Parsing for If-Then Recipes via Hierarchical Reinforcement Learning
Given a text description, most existing semantic parsers synthesize a program in one shot.
Hierarchical Reinforcement Learning
reinforcement-learning
+2
StaQC: A Systematically Mined Question-Code Dataset from Stack Overflow
In this paper, we investigate a new problem of systematically mining question-code pairs from Stack Overflow (in contrast to heuristically collecting them).
