32 citations · 48 across the 19 of their papers we have counts for
19 papers
Detecting Corpus-Level Knowledge Inconsistencies in Wikipedia with Large Language Models
Sina J. Semnani, Jirayu Burapacheep, Arpandeep Khatua +3
Wikipedia is the largest open knowledge corpus, widely used worldwide and serving as a key resource for training large language models (LLMs) and retrieval-augmented generation (RA…
CHURRO: Making History Readable with an Open-Weight Large Vision-Language Model for High-Accuracy, Low-Cost Historical Text Recognition
Sina J. Semnani, Han Zhang, Xinyan He +2
Accurate text recognition for historical documents can greatly advance the study and preservation of cultural heritage. Existing vision-language models (VLMs), however, are designe…
LEMONADE: A Large Multilingual Expert-Annotated Abstractive Event Dataset for the Real World
Sina J. Semnani, Pingyue Zhang, Wanyue Zhai +6
This paper presents LEMONADE, a large-scale conflict event dataset comprising 39,786 events across 20 languages and 171 countries, with extensive coverage of region-specific entiti…
Into the Unknown Unknowns: Engaged Human Learning through Participation in Language Model Agent Conversations
Yucheng Jiang, Yijia Shao, Dekun Ma +2
While language model (LM)-powered chatbots and generative search engines excel at answering concrete queries, discovering information in the terrain of unknown unknowns remains cha…
SPINACH: SPARQL-Based Information Navigation for Challenging Real-World Questions
Shicheng Liu, Sina J. Semnani, Harold Triedman +3
Large Language Models (LLMs) have led to significant improvements in the Knowledge Base Question Answering (KBQA) task. However, datasets used in KBQA studies do not capture the tr…
Zero-shot Persuasive Chatbots with LLM-Generated Strategies and Information Retrieval
Kazuaki Furumai, Roberto Legaspi, Julio Vizcarra +6
Persuasion plays a pivotal role in a wide range of applications from health intervention to the promotion of social good. Persuasive chatbots employed responsibly for social good c…
Benchmarks Underestimate the Readiness of Multi-lingual Dialogue Agents
Andrew H. Lee, Sina J. Semnani, Galo Castillo-López +16
Creating multilingual task-oriented dialogue (TOD) agents is challenging due to the high cost of training data acquisition. Following the research trend of improving training data…
SPAGHETTI: Open-Domain Question Answering from Heterogeneous Data Sources with Retrieval and Semantic Parsing
Heidi C. Zhang, Sina J. Semnani, Farhad Ghassemi +3
We introduce SPAGHETTI: Semantic Parsing Augmented Generation for Hybrid English information from Text Tables and Infoboxes, a hybrid question-answering (QA) pipeline that utilizes…
SUQL: Conversational Search over Structured and Unstructured Data with Large Language Models
Shicheng Liu, Jialiang Xu, Wesley Tjangnaka +3
While most conversational agents are grounded on either free-text or structured knowledge, many knowledge corpora consist of hybrid sources. This paper presents the first conversat…
X-RiSAWOZ: High-Quality End-to-End Multilingual Dialogue Datasets and Few-shot Agents
Mehrad Moradshahi, Tianhao Shen, Kalika Bali +16
Task-oriented dialogue research has mainly focused on a few popular languages like English and Chinese, due to the high dataset creation cost for a new language. To reduce the cost…
WikiChat: Stopping the Hallucination of Large Language Model Chatbots by Few-Shot Grounding on Wikipedia
Sina J. Semnani, Violet Z. Yao, Heidi C. Zhang +1
This paper presents the first few-shot LLM-based chatbot that almost never hallucinates and has high conversationality and low latency. WikiChat is grounded on the English Wikipedi…
Fine-tuned LLMs Know More, Hallucinate Less with Few-Shot Sequence-to-Sequence Semantic Parsing over Wikidata
Silei Xu, Shicheng Liu, Theo Culhane +4
While large language models (LLMs) can answer many questions correctly, they can also hallucinate and give wrong answers. Wikidata, with its over 12 billion facts, can be used to g…
Zero and Few-Shot Localization of Task-Oriented Dialogue Agents with a Distilled Representation
Mehrad Moradshahi, Sina J. Semnani, Monica S. Lam
Task-oriented Dialogue (ToD) agents are mostly limited to a few widely-spoken languages, mainly due to the high cost of acquiring training data for each language. Existing low-cost…
ThingTalk: An Extensible, Executable Representation Language for Task-Oriented Dialogues
Monica S. Lam, Giovanni Campagna, Mehrad Moradshahi +2
Task-oriented conversational agents rely on semantic parsers to translate natural language to formal representations. In this paper, we propose the design and rationale of the Thin…
House Price Prediction using Satellite Imagery
Sina Jandaghi Semnani, Hoormazd Rezaei
In this paper we show how using satellite images can improve the accuracy of housing price estimation models. Using Los Angeles County's property assessment dataset, by transferrin…
Localizing Open-Ontology QA Semantic Parsers in a Day Using Machine Translation
Mehrad Moradshahi, Giovanni Campagna, Sina J. Semnani +2
We propose Semantic Parser Localizer (SPL), a toolkit that leverages Neural Machine Translation (NMT) systems to localize a semantic parser for a new language. Our methodology is t…
AutoQA: From Databases To QA Semantic Parsers With Only Synthetic Training Data
Silei Xu, Sina J. Semnani, Giovanni Campagna +1
We propose AutoQA, a methodology and toolkit to generate semantic parsers that answer questions on databases, with no manual effort. Given a database schema and its data, AutoQA au…
Revisiting the Open-Domain Question Answering Pipeline
Sina J. Semnani, Manish Pandey
Open-domain question answering (QA) is the tasl of identifying answers to natural questions from a large corpus of documents. The typical open-domain QA system starts with informat…
A Few-Shot Semantic Parser for Wizard-of-Oz Dialogues with the Precise ThingTalk Representation
Giovanni Campagna, Sina J. Semnani, Ryan Kearns +3
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.…