2 citations · 3 across the 11 of their papers we have counts for
12 papers
SHELDON: Reasoning through Dual Search over Hypothesis and Observation Spaces
Harshit Joshi, Priyank Shethia, Monica S. Lam
Test-time scaling has enabled LLMs to solve increasingly complex reasoning, discovery, and optimization tasks. However, existing approaches often gather observations while seeking…
SLIDERS: Systematic Reviews via Automated Evidence Synthesis and Reconciliation
Harshit Joshi, Priyank Shethia, Jadelynn Dao +1
Systematic reviews -- which requires comprehensive evidence collection and synthesis from large document corpora in response to targeted research questions -- are foundational in f…
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Accurate text recognition for historical documents can greatly advance the study and preservation of cultural heritage.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…
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…
Controllable and Reliable Knowledge-Intensive Task-Oriented Conversational Agents with Declarative Genie Worksheets
Harshit Joshi, Shicheng Liu, James Chen +2
Large Language Models can carry out human-like conversations in diverse settings, responding to user requests for tasks and knowledge. However, existing conversational agents imple…
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…
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…