Search Results for author:
Found 8 papers, 5 papers with code
When is Tree Search Useful for LLM Planning? It Depends on the Discriminator
In this paper, we examine how large language models (LLMs) solve multi-step problems under a language agent framework with three components: a generator, a discriminator, and a planning method.
eCeLLM: Generalizing Large Language Models for E-commerce from Large-scale, High-quality Instruction Data
Both the ECInstruct dataset and the eCeLLM models show great potential in empowering versatile and effective LLMs for e-commerce.
Roll Up Your Sleeves: Working with a Collaborative and Engaging Task-Oriented Dialogue System
We introduce TacoBot, a user-centered task-oriented digital assistant designed to guide users through complex real-world tasks with multiple steps.
Exploring Chain-of-Thought Style Prompting for Text-to-SQL
Thus, we systematically study how to enhance LLMs' reasoning ability through chain of thought (CoT) style prompting, including the original chain-of-thought prompting (Wei et al., 2022b) and least-to-most prompting (Zhou et al., 2023).
Error Detection for Text-to-SQL Semantic Parsing
Despite remarkable progress in text-to-SQL semantic parsing in recent years, the performance of existing parsers is still far from perfect.
Text-to-SQL Error Correction with Language Models of Code
Thus, we propose a novel representation for SQL queries and their edits that adheres more closely to the pre-training corpora of language models of code.
Automatic Evaluation of Attribution by Large Language Models
We manually curate a set of test examples covering 12 domains from a generative search engine, New Bing.
Bootstrapping a User-Centered Task-Oriented Dialogue System
We present TacoBot, a task-oriented dialogue system built for the inaugural Alexa Prize TaskBot Challenge, which assists users in completing multi-step cooking and home improvement tasks.
