1 citations · 2 across the 5 of their papers we have counts for
5 papers
Reflective Agreement: Combining Self-Mixture of Agents with a Sequence Tagger for Robust Event Extraction
Fatemeh Haji, Mazal Bethany, Cho-Yu Jason Chiang +2
Event Extraction (EE) involves automatically identifying and extracting structured information about events from unstructured text, including triggers, event types, and arguments.…
Improving LLM Reasoning with Multi-Agent Tree-of-Thought Validator Agent
Fatemeh Haji, Mazal Bethany, Maryam Tabar +3
Multi-agent strategies have emerged as a promising approach to enhance the reasoning abilities of Large Language Models (LLMs) by assigning specialized roles in the problem-solving…
Enhancing Event Reasoning in Large Language Models through Instruction Fine-Tuning with Semantic Causal Graphs
Mazal Bethany, Emet Bethany, Brandon Wherry +4
Event detection and text reasoning have become critical applications across various domains. While LLMs have recently demonstrated impressive progress in reasoning abilities, they…
RASTeR: Robust, Agentic, and Structured Temporal Reasoning
Dan Schumacher, Fatemeh Haji, Tara Grey +7
Temporal question answering (TQA) remains a challenge for large language models (LLMs), particularly when retrieved content may be irrelevant, outdated, or temporally inconsistent.…
Deciphering Textual Authenticity: A Generalized Strategy through the Lens of Large Language Semantics for Detecting Human vs. Machine-Generated Text
Mazal Bethany, Brandon Wherry, Emet Bethany +3
With the recent proliferation of Large Language Models (LLMs), there has been an increasing demand for tools to detect machine-generated text. The effective detection of machine-ge…