Search Results for author:
Found 10 papers, 9 papers with code
SUQL: Conversational Search over Structured and Unstructured Data with Large Language Models
This paper presents the first conversational agent that supports the full generality of hybrid data access for large knowledge corpora, through a language we developed called SUQL (Structured and Unstructured Query Language).
X-RiSAWOZ: High-Quality End-to-End Multilingual Dialogue Datasets and Few-shot Agents
We create a new multilingual benchmark, X-RiSAWOZ, by translating the Chinese RiSAWOZ to 4 languages: English, French, Hindi, Korean; and a code-mixed English-Hindi language.
WikiChat: Stopping the Hallucination of Large Language Model Chatbots by Few-Shot Grounding on Wikipedia
WikiChat generates a response from an LLM, retains only the grounded facts, and combines them with additional information it retrieves from the corpus to form factual and engaging responses.
Fine-tuned LLMs Know More, Hallucinate Less with Few-Shot Sequence-to-Sequence Semantic Parsing over Wikidata
By pairing our semantic parser with GPT-3, we combine verifiable results with qualified GPT-3 guesses to provide useful answers to 96% of the questions in dev.
Zero and Few-Shot Localization of Task-Oriented Dialogue Agents with a Distilled Representation
We propose automatic methods that use ToD training data in a source language to build a high-quality functioning dialogue agent in another target language that has no training data (i. e. zero-shot) or a small training set (i. e. few-shot).
ThingTalk: An Extensible, Executable Representation Language for Task-Oriented Dialogues
Task-oriented conversational agents rely on semantic parsers to translate natural language to formal representations.
Localizing Open-Ontology QA Semantic Parsers in a Day Using Machine Translation
We propose Semantic Parser Localizer (SPL), a toolkit that leverages Neural Machine Translation (NMT) systems to localize a semantic parser for a new language.
AutoQA: From Databases To QA Semantic Parsers With Only Synthetic Training Data
To demonstrate the generality of AutoQA, we also apply it to the Overnight dataset.
A Few-Shot Semantic Parser for Wizard-of-Oz Dialogues with the Precise ThingTalk Representation
Previous attempts to build effective semantic parsers for Wizard-of-Oz (WOZ) conversations suffer from the difficulty in acquiring a high-quality, manually annotated training set.
Revisiting the Open-Domain Question Answering Pipeline
Open-domain question answering (QA) is the tasl of identifying answers to natural questions from a large corpus of documents.
