10 papers · 1 filter
Decomposed Prompting Does Not Fix Knowledge Gaps, But Helps Models Say "I Don't Know"
Dhruv Madhwal, Lyuxin David Zhang, Dan Roth +2
Large language models often struggle to recognize their knowledge limits in closed-book question answering, leading to confident hallucinations. While decomposed prompting is typic…
Evaluating LLMs' Mathematical Reasoning in Financial Document Question Answering
Pragya Srivastava, Manuj Malik, Vivek Gupta +2
Large Language Models (LLMs), excel in natural language understanding, but their capability for complex mathematical reasoning with an amalgamation of structured tables and unstruc…
No Universal Prompt: Unifying Reasoning through Adaptive Prompting for Temporal Table Reasoning
Abhishek Rajgaria, Kushagra Dixit, Mayank Vyas +3
Temporal Table Reasoning is a critical challenge for Large Language Models (LLMs), requiring effective reasoning to extract relevant insights. Despite existence of multiple prompti…
PRAISE: Enhancing Product Descriptions with LLM-Driven Structured Insights
Adnan Qidwai, Srija Mukhopadhyay, Prerana Khatiwada +2
Accurate and complete product descriptions are crucial for e-commerce, yet seller-provided information often falls short. Customer reviews offer valuable details but are laborious…
LLM-Symbolic Integration for Robust Temporal Tabular Reasoning
Atharv Kulkarni, Kushagra Dixit, Vivek Srikumar +2
Temporal tabular question answering presents a significant challenge for Large Language Models (LLMs), requiring robust reasoning over structured data, which is a task where tradit…
UNJOIN: Enhancing Multi-Table Text-to-SQL Generation via Schema Simplification
Poojah Ganesan, Rajat Aayush Jha, Dan Roth +1
Recent advances in large language models (LLMs) have greatly improved Text-to-SQL performance for single-table queries. But, it remains challenging in multi-table databases due to…