Search Results for author:
Found 35 papers, 6 papers with code
Gradient-guided Unsupervised Lexically Constrained Text Generation
Previous works usually apply beam-search-based methods or stochastic searching methods to lexically-constrained generation.
Towards a Unified View of Preference Learning for Large Language Models: A Survey
Finally, based on our unified perspective, we explore the challenges and future research directions for aligning large language models with human preferences.
HSF: Defending against Jailbreak Attacks with Hidden State Filtering
With the growing deployment of LLMs in daily applications like chatbots and content generation, efforts to ensure outputs align with human values and avoid harmful content have intensified.
ATM: Adversarial Tuning Multi-agent System Makes a Robust Retrieval-Augmented Generator
The ATM steers the Generator to have a robust perspective of useful documents for question answering with the help of an auxiliary Attacker agent.
Unlocking Multi-View Insights in Knowledge-Dense Retrieval-Augmented Generation
While Retrieval-Augmented Generation (RAG) plays a crucial role in the application of Large Language Models (LLMs), existing retrieval methods in knowledge-dense domains like law and medicine still suffer from a lack of multi-perspective views, which are essential for improving interpretability and reliability.
Large Language Models Are Unconscious of Unreasonability in Math Problems
Large language models (LLMs) demonstrate substantial capabilities in solving math problems.
ShieldLM: Empowering LLMs as Aligned, Customizable and Explainable Safety Detectors
The safety of Large Language Models (LLMs) has gained increasing attention in recent years, but there still lacks a comprehensive approach for detecting safety issues within LLMs' responses in an aligned, customizable and explainable manner.
ASETF: A Novel Method for Jailbreak Attack on LLMs through Translate Suffix Embeddings
In addition, our approach can be generalized into a broader method for generating transferable adversarial suffixes that can successfully attack multiple LLMs, even black-box LLMs, such as ChatGPT and Gemini.
Harnessing the Plug-and-Play Controller by Prompting
The proposed approach aims to enhance the fluency of generated text by guiding the generation process with PPCs.
Text Attribute Control via Closed-Loop Disentanglement
In this approach, we use a semi-supervised contrastive learning method to encourage the disentanglement of attributes in latent spaces.
Rationalizing Predictions by Adversarial Information Calibration
One form of explanation for a prediction is an extractive rationale, i. e., a subset of features of an instance that lead the model to give its prediction on that instance.
A Stable, Fast, and Fully Automatic Learning Algorithm for Predictive Coding Networks
Predictive coding networks are neuroscience-inspired models with roots in both Bayesian statistics and neuroscience.
Bird-Eye Transformers for Text Generation Models
Transformers have become an indispensable module for text generation models since their great success in machine translation.
Small Changes Make Big Differences: Improving Multi-turn Response Selection in Dialogue Systems via Fine-Grained Contrastive Learning
The sequence representation plays a key role in the learning of matching degree between the dialogue context and the response.
Rationale production to support clinical decision-making
In this work, we apply InfoCal, the current state-of-the-art model that produces extractive rationales for its predictions, to the task of predicting hospital readmission using hospital discharge notes.
RecInDial: A Unified Framework for Conversational Recommendation with Pretrained Language Models
Furthermore, we propose to evaluate the CRS models in an end-to-end manner, which can reflect the overall performance of the entire system rather than the performance of individual modules, compared to the separate evaluations of the two modules used in previous work.
Unifying Categorical Models by Explicit Disentanglement of the Labels' Generative Factors
By doing so, it is possible to merge multiple datasets based on different categorical models by projecting the data points into a unified latent space.
Associative Memories via Predictive Coding
We conclude by discussing the possible impact of this work in the neuroscience community, by showing that our model provides a plausible framework to study learning and retrieval of memories in the brain, as it closely mimics the behavior of the hippocampus as a memory index and generative model.
Controlling Text Edition by Changing Answers of Specific Questions
Experimental results on the test set show that our proposed method is a good fit for this novel NLP task.
Multi-type Disentanglement without Adversarial Training
After the latent space is disentangled, the style of a sentence can be transformed by tuning the style representation without affecting other features of the sentence.
Learning from the Best: Rationalizing Prediction by Adversarial Information Calibration
We use an adversarial-based technique to calibrate the information extracted by the two models such that the difference between them is an indicator of the missed or over-selected features.
Auto-Dialabel: Labeling Dialogue Data with Unsupervised Learning
The lack of labeled data is one of the main challenges when building a task-oriented dialogue system.
Table-to-text Generation by Structure-aware Seq2seq Learning
In the decoding phase, dual attention mechanism which contains word level attention and field level attention is proposed to model the semantic relevance between the generated description and the table.
Ranked #1 on
Table-to-Text Generation
on WikiBio
Order-Planning Neural Text Generation From Structured Data
Generating texts from structured data (e. g., a table) is important for various natural language processing tasks such as question answering and dialog systems.
Syntax Aware LSTM model for Semantic Role Labeling
In Semantic Role Labeling (SRL) task, the tree structured dependency relation is rich in syntax information, but it is not well handled by existing models.
A Progressive Learning Approach to Chinese SRL Using Heterogeneous Data
But the training data of single corpus is often limited.
Syntax Aware LSTM Model for Chinese Semantic Role Labeling
As for semantic role labeling (SRL) task, when it comes to utilizing parsing information, both traditional methods and recent recurrent neural network (RNN) based methods use the feature engineering way.
Towards Time-Aware Knowledge Graph Completion
In this paper, we present a novel time-aware knowledge graph completion model that is able to predict links in a KG using both the existing facts and the temporal information of the facts.
Reading and Thinking: Re-read LSTM Unit for Textual Entailment Recognition
After read the premise again, the model can get a better understanding of the premise, which can also affect the understanding of the hypothesis.
Ranked #40 on
Natural Language Inference
on SNLI
Joint Learning Templates and Slots for Event Schema Induction
Automatic event schema induction (AESI) means to extract meta-event from raw text, in other words, to find out what types (templates) of event may exist in the raw text and what roles (slots) may exist in each event type.
