Search Results for author:
Found 21 papers, 9 papers with code
Neural Conversational QA: Learning to Reason vs Exploiting Patterns
Neural Conversational QA tasks such as ShARC require systems to answer questions based on the contents of a given passage.
On the Standardization of Behavioral Use Clauses and Their Adoption for Responsible Licensing of AI
As of the end of 2023, on the order of 40, 000 software and model repositories have adopted responsible AI licenses licenses.
Prompting with Pseudo-Code Instructions
Prompting with natural language instructions has recently emerged as a popular method of harnessing the capabilities of large language models.
StarCoder: may the source be with you!
The BigCode community, an open-scientific collaboration working on the responsible development of Large Language Models for Code (Code LLMs), introduces StarCoder and StarCoderBase: 15. 5B parameter models with 8K context length, infilling capabilities and fast large-batch inference enabled by multi-query attention.
Ranked #42 on
Code Generation
on MBPP
SantaCoder: don't reach for the stars!
The BigCode project is an open-scientific collaboration working on the responsible development of large language models for code.
Semi-Structured Object Sequence Encoders
In this paper we explore the task of modeling semi-structured object sequences; in particular, we focus our attention on the problem of developing a structure-aware input representation for such sequences.
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions.
Joint Reasoning on Hybrid-knowledge sources for Task-Oriented Dialog
Traditional systems designed for task oriented dialog utilize knowledge present only in structured knowledge sources to generate responses.
Mix-and-Match: Scalable Dialog Response Retrieval using Gaussian Mixture Embeddings
In this paper, we combine the best of both worlds by proposing a scalable model that can learn complex relationships between context-response pairs.
Variational Learning for Unsupervised Knowledge Grounded Dialogs
Recent methods for knowledge grounded dialogs generate responses by incorporating information from an external textual document.
Simulated Chats for Building Dialog Systems: Learning to Generate Conversations from Instructions
Popular dialog datasets such as MultiWOZ are created by providing crowd workers an instruction, expressed in natural language, that describes the task to be accomplished.
Agent Assist through Conversation Analysis
Customer support agents play a crucial role as an interface between an organization and its end-users.
Joint Spatio-Textual Reasoning for Answering Tourism Questions
In response, we develop the first joint spatio-textual reasoning model, which combines geo-spatial knowledge with information in textual corpora to answer questions.
Neural Conversational QA: Learning to Reason v.s. Exploiting Patterns
On studying recent state-of-the-art models on the ShARCQA task, we found indications that the models learn spurious clues/patterns in the dataset.
Large Scale Question Answering using Tourism Data
We introduce the novel task of answering entity-seeking recommendation questions using a collection of reviews that describe candidate answer entities.
Multi-level Memory for Task Oriented Dialogs
Instead of using triples to store KB results, we introduce a novel multi-level memory architecture consisting of cells for each query and their corresponding results.
Exemplar Encoder-Decoder for Neural Conversation Generation
In this paper we present the Exemplar Encoder-Decoder network (EED), a novel conversation model that learns to utilize \textit{similar} examples from training data to generate responses.
Document Chunking and Learning Objective Generation for Instruction Design
Instructional Systems Design is the practice of creating of instructional experiences that make the acquisition of knowledge and skill more efficient, effective, and appealing.
Towards Understanding and Answering Multi-Sentence Recommendation Questions on Tourism
We introduce the first system towards the novel task of answering complex multisentence recommendation questions in the tourism domain.
