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Found 57 papers, 28 papers with code
Iterative Self-Tuning LLMs for Enhanced Jailbreaking Capabilities
Recent research has shown that Large Language Models (LLMs) are vulnerable to automated jailbreak attacks, where adversarial suffixes crafted by algorithms appended to harmful queries bypass safety alignment and trigger unintended responses.
ExACT: Teaching AI Agents to Explore with Reflective-MCTS and Exploratory Learning
Autonomous agents have demonstrated significant potential in automating complex multistep decision-making tasks.
Rethinking Interpretability in the Era of Large Language Models
We highlight two emerging research priorities for LLM interpretation: using LLMs to directly analyze new datasets and to generate interactive explanations.
Teaching Language Models to Self-Improve through Interactive Demonstrations
The self-improving ability of large language models (LLMs), enabled by prompting them to analyze and revise their own outputs, has garnered significant interest in recent research.
MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts
To bridge this gap, we present MathVista, a benchmark designed to combine challenges from diverse mathematical and visual tasks.
Self-Verification Improves Few-Shot Clinical Information Extraction
Extracting patient information from unstructured text is a critical task in health decision-support and clinical research.
Self-Checker: Plug-and-Play Modules for Fact-Checking with Large Language Models
Fact-checking is an essential task in NLP that is commonly utilized for validating the factual accuracy of claims.
Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models
At the heart of Chameleon is an LLM-based planner that assembles a sequence of tools to execute to generate the final response.
Instruction Tuning with GPT-4
Prior work has shown that finetuning large language models (LLMs) using machine-generated instruction-following data enables such models to achieve remarkable zero-shot capabilities on new tasks, and no human-written instructions are needed.
Interactive Text Generation
Unfortunately, this means most of the research on text, code, and image generation has focused on non-interactive settings, whereby the model is expected to get everything right without accounting for any input from a user who may be willing to help.
Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback
Large language models (LLMs), such as ChatGPT, are able to generate human-like, fluent responses for many downstream tasks, e. g., task-oriented dialog and question answering.
Guiding Large Language Models via Directional Stimulus Prompting
Our experiments demonstrate that the framework consistently improves LLMs' (e. g., ChatGPT, Codex, InstructGPT) performance on these supervised tasks using minimal labeled data.
DIONYSUS: A Pre-trained Model for Low-Resource Dialogue Summarization
In this work, we propose DIONYSUS (dynamic input optimization in pre-training for dialogue summarization), a pre-trained encoder-decoder model for summarizing dialogues in any new domain.
Enhancing Task Bot Engagement with Synthesized Open-Domain Dialog
To better mimic human-level conversations that usually fuse various dialog modes, it is essential to build a system that can effectively handle both TOD and ODD and access different knowledge sources.
Grounded Keys-to-Text Generation: Towards Factual Open-Ended Generation
We propose a new grounded keys-to-text generation task: the task is to generate a factual description about an entity given a set of guiding keys, and grounding passages.
GODEL: Large-Scale Pre-Training for Goal-Directed Dialog
We introduce GODEL (Grounded Open Dialogue Language Model), a large pre-trained language model for dialog.
Probing Factually Grounded Content Transfer with Factual Ablation
Particularly, this domain allows us to introduce the notion of factual ablation for automatically measuring factual consistency: this captures the intuition that the model should be less likely to produce an output given a less relevant grounding document.
NeurIPS 2021 Competition IGLU: Interactive Grounded Language Understanding in a Collaborative Environment
Starting from a very young age, humans acquire new skills and learn how to solve new tasks either by imitating the behavior of others or by following provided natural language instructions.
Automatic Document Sketching: Generating Drafts from Analogous Texts
The advent of large pre-trained language models has made it possible to make high-quality predictions on how to add or change a sentence in a document.
RetGen: A Joint framework for Retrieval and Grounded Text Generation Modeling
We propose a framework that alleviates this data constraint by jointly training a grounded generator and document retriever on the language model signal.
An Adversarially-Learned Turing Test for Dialog Generation Models
To alleviate this risk, we propose an adversarial training approach to learn a robust model, ATT (Adversarial Turing Test), that discriminates machine-generated responses from human-written replies.
Ask what's missing and what's useful: Improving Clarification Question Generation using Global Knowledge
The ability to generate clarification questions i. e., questions that identify useful missing information in a given context, is important in reducing ambiguity.
Data Augmentation for Abstractive Query-Focused Multi-Document Summarization
The progress in Query-focused Multi-Document Summarization (QMDS) has been limited by the lack of sufficient largescale high-quality training datasets.
Text Editing by Command
A prevailing paradigm in neural text generation is one-shot generation, where text is produced in a single step.
Dialogue Response Ranking Training with Large-Scale Human Feedback Data
Particularly, our ranker outperforms the conventional dialog perplexity baseline with a large margin on predicting Reddit feedback.
DIALOGPT : Large-Scale Generative Pre-training for Conversational Response Generation
We present a large, tunable neural conversational response generation model, DIALOGPT (dialogue generative pre-trained transformer).
MixingBoard: a Knowledgeable Stylized Integrated Text Generation Platform
We present MixingBoard, a platform for quickly building demos with a focus on knowledge grounded stylized text generation.
A Controllable Model of Grounded Response Generation
Current end-to-end neural conversation models inherently lack the flexibility to impose semantic control in the response generation process, often resulting in uninteresting responses.
The Eighth Dialog System Technology Challenge
This paper introduces the Eighth Dialog System Technology Challenge.
DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation
We present a large, tunable neural conversational response generation model, DialoGPT (dialogue generative pre-trained transformer).
Structuring Latent Spaces for Stylized Response Generation
This structure allows the system to generate stylized relevant responses by sampling in the neighborhood of the conversation model prediction, and continuously control the style level.
Microsoft Icecaps: An Open-Source Toolkit for Conversation Modeling
The Intelligent Conversation Engine: Code and Pre-trained Systems (Microsoft Icecaps) is an upcoming open-source natural language processing repository.
Conversing by Reading: Contentful Neural Conversation with On-demand Machine Reading
Although neural conversation models are effective in learning how to produce fluent responses, their primary challenge lies in knowing what to say to make the conversation contentful and non-vacuous.
Consistent Dialogue Generation with Self-supervised Feature Learning
Generating responses that are consistent with the dialogue context is one of the central challenges in building engaging conversational agents.
Jointly Optimizing Diversity and Relevance in Neural Response Generation
In this paper, we propose a SpaceFusion model to jointly optimize diversity and relevance that essentially fuses the latent space of a sequence-to-sequence model and that of an autoencoder model by leveraging novel regularization terms.
Ranked #1 on
Dialogue Generation
on Reddit (multi-ref)
Dialog System Technology Challenge 7
This paper introduces the Seventh Dialog System Technology Challenges (DSTC), which use shared datasets to explore the problem of building dialog systems.
Towards Coherent and Cohesive Long-form Text Generation
Generating coherent and cohesive long-form texts is a challenging task.
Neural Approaches to Conversational AI
The present paper surveys neural approaches to conversational AI that have been developed in the last few years.
Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization
Responses generated by neural conversational models tend to lack informativeness and diversity.
Multi-Task Learning for Speaker-Role Adaptation in Neural Conversation Models
Building a persona-based conversation agent is challenging owing to the lack of large amounts of speaker-specific conversation data for model training.
A Knowledge-Grounded Neural Conversation Model
We generalize the widely-used Seq2Seq approach by conditioning responses on both conversation history and external "facts", allowing the model to be versatile and applicable in an open-domain setting.
Image-Grounded Conversations: Multimodal Context for Natural Question and Response Generation
The popularity of image sharing on social media and the engagement it creates between users reflects the important role that visual context plays in everyday conversations.
Deep Reinforcement Learning for Dialogue Generation
Recent neural models of dialogue generation offer great promise for generating responses for conversational agents, but tend to be shortsighted, predicting utterances one at a time while ignoring their influence on future outcomes.
Visual Storytelling
We introduce the first dataset for sequential vision-to-language, and explore how this data may be used for the task of visual storytelling.
A Persona-Based Neural Conversation Model
We present persona-based models for handling the issue of speaker consistency in neural response generation.
A Diversity-Promoting Objective Function for Neural Conversation Models
Sequence-to-sequence neural network models for generation of conversational responses tend to generate safe, commonplace responses (e. g., "I don't know") regardless of the input.
A Survey of Current Datasets for Vision and Language Research
Integrating vision and language has long been a dream in work on artificial intelligence (AI).
deltaBLEU: A Discriminative Metric for Generation Tasks with Intrinsically Diverse Targets
We introduce Discriminative BLEU (deltaBLEU), a novel metric for intrinsic evaluation of generated text in tasks that admit a diverse range of possible outputs.
A Neural Network Approach to Context-Sensitive Generation of Conversational Responses
We present a novel response generation system that can be trained end to end on large quantities of unstructured Twitter conversations.
