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Works126 from public data
- Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback552
A LLM-Augmenter system, which augments a black-box LLM with a set of plug-and-play modules to significantly reduces ChatGPT's hallucinations without sacrificing the fluency and informativeness of its responses.
- An Introduction to Computational Networks and the Computational Network Toolkit403
The computational network toolkit (CNTK), an implementation of CN that supports both GPU and CPU, is introduced and the architecture and the key components of the CNTK are described, the command line options to use C NTK, and the network definition and model editing language are described.
- Latent Action Pretraining from Videos382
Experimental results demonstrate that the LAPA method significantly outperforms existing techniques that train robot manipulation policies from large-scale videos and outperforms the state-of-the-art VLA model trained with robotic action labels on real-world manipulation tasks that require language conditioning, generalization to unseen objects, and semantic generalization to unseen instructions.
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- Task-oriented Dialogue System for Automatic Diagnosis255
Experimental results on this dialogue system show that additional symptoms extracted from conversation can greatly improve the accuracy for disease identification and the dialogue system is able to collect these symptoms automatically and make a better diagnosis.
- S oloist : BuildingTask Bots at Scale with Transfer Learning and Machine Teaching230
A new method that uses transfer learning and machine teaching to build task bots at scale, Soloist, is presented, which parameterize classical modular task-oriented dialog systems using a Transformer-based auto-regressive language model, which subsumes different dialog modules into a single neural model.
- Few-shot Natural Language Generation for Task-Oriented Dialog213
FewshotWOZ is presented, the first NLG benchmark to simulate the few-shot learning setting in task-oriented dialog systems, and the proposed SC-GPT model significantly outperforms existing methods, measured by various automatic metrics and human evaluations.
- Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space205
This paper proposes the first large-scale language VAE model, Optimus, a universal latent embedding space for sentences that is first pre-trained on large text corpus, and then fine-tuned for various language generation and understanding tasks.
- Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning197
This paper addresses the travel planning task by formulating the task in the mathematical framework of options over Markov Decision Processes (MDPs), and proposing a hierarchical deep reinforcement learning approach to learning a dialogue manager that operates at different temporal scales.
- Magma: A Foundation Model for Multimodal AI Agents185
Magma is a foundation model that serves multimodal AI agentic tasks in both the digital and physical worlds and creates new state-of-the-art results on UI navigation and robotic manipulation tasks, outperforming previous models that are specifically tailored to these tasks.
- Deep Dyna-Q: Integrating Planning for Task-Completion Dialogue Policy Learning184
Deep Dyna-Q is presented, which to the authors' knowledge is the first deep RL framework that integrates planning for task-completion dialogue policy learning and incorporates into the dialogue agent a model of the environment, referred to as the world model, to mimic real user response and generate simulated experience.
- Explanations from Large Language Models Make Small Reasoners Better181
This paper considers the problem of leveraging the explanations generated by LLM to improve the training of small reasoners, which are more favorable in real-production deployment due to their low cost.
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- Integrating planning for task-completion dialogue policy learning.147
This paper addresses challenges of training a task-completion dialogue agent with real users via reinforcement learning by integrating planning into the dialogue policy learning based on Dyna-Q framework, and provides a more sample-efficient approach to learn the dialogue polices.
- CollabLLM: From Passive Responders to Active Collaborators145
CollabLLM is introduced, a novel and general training framework that enhances multiturn human-LLM collaboration and estimates the long-term contribution of responses using Multiturn-aware Rewards by reinforcement fine-tuning these rewards.
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- SOLOIST: Few-shot Task-Oriented Dialog with A Single Pre-trained Auto-regressive Model127
A new method SOLOIST is presented, which uses transfer learning to efficiently build task-oriented dialog systems at scale using a Transformer-based auto-regressive language model, which subsumes different dialog modules into a single neural model.
- ConvLab-2: An Open-Source Toolkit for Building, Evaluating, and Diagnosing Dialogue Systems121
ConvLab-2, an open-source toolkit that enables researchers to build task-oriented dialogue systems with state-of-the-art models, perform an end-to-end evaluation, and diagnose the weakness of systems is presented.
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- ConvLab: Multi-Domain End-to-End Dialog System Platform117
ConvLab is presented, an open-source multi-domain end-to-end dialog system platform that enables researchers to quickly set up experiments with reusable components and compare a large set of different approaches, ranging from conventional pipeline systems to end- to-end neural models, in common environments.
- Few-Shot Named Entity Recognition: An Empirical Baseline Study110
Three orthogonal schemes to improve model generalization ability in few-shot settings are investigated: meta-learning to construct prototypes for different entity types, task-specific supervised pre-training on noisy web data to extract entity-related representations and self-training to leverage unlabeled in-domain data.
- Evaluating the Instruction-Following Robustness of Large Language Models to Prompt Injection92
A benchmark is established to evaluate the robustness of instruction-following LLMs against prompt injection attacks, assessing their ability to discern which instructions to follow and which to disregard, and shows that certain models are excessively inclined to prioritize embedded instructions in prompts.
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- GODEL: Large-Scale Pre-Training for Goal-Directed Dialog80
GODEL (Grounded Open Dialogue Language Model) is introduced, a large pre-trained language model for dialog that leverages a new phase of grounded pre-training designed to better support adapting GODEL to a wide range of downstream dialog tasks that require information external to the current conversation to produce good responses.
- Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning76
This paper presents a new method - adversarial advantage actor-critic (Adversarial A2C), which significantly improves the efficiency of dialogue policy learning in task-completion dialogue systems.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-16. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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