8 papers
TabRank: Chain-of-Thought Distillation for Table Re-Rankers
Adarsh Singh, Kushal Raj Bhandari, Jianxi Gao +2
The ability to retrieve relevant tables for answering questions is a key task for structured information retrieval. Multi-stage retrieval systems rely heavily on rerankers to refin…
Evoflux: Inference-Time Evolution of Executable Tool Workflows for Compact Agents
Kushal Raj Bhandari, Ling Yue, Ching-Yun Ko +4
Compact language models (LMs) reduce cost, latency, and deployment risk for tool agents. Yet MCP-style tool use requires more than isolated function calling: an agent must discover…
Improving Robustness of Tabular Retrieval via Representational Stability
Kushal Raj Bhandari, Adarsh Singh, Jianxi Gao +2
Transformer-based table retrieval systems flatten structured tables into token sequences, making retrieval sensitive to the choice of serialization even when table semantics remain…
CRAFT: Training-Free Cascaded Retrieval for Tabular QA
Adarsh Singh, Kushal Raj Bhandari, Jianxi Gao +2
Open-Domain Table Question Answering (TQA) involves retrieving relevant tables from a large corpus to answer natural language queries. Traditional dense retrieval models such as DT…
From Static Templates to Dynamic Runtime Graphs: A Survey of Workflow Optimization for LLM Agents
Ling Yue, Kushal Raj Bhandari, Ching-Yun Ko +6
Large language model (LLM)-based systems are becoming increasingly popular for solving tasks by constructing executable workflows that interleave LLM calls, information retrieval,…
Exploring the Robustness of Language Models for Tabular Question Answering via Attention Analysis
Kushal Raj Bhandari, Sixue Xing, Soham Dan +1
Large Language Models (LLMs), already shown to ace various unstructured text comprehension tasks, have also remarkably been shown to tackle table (structured) comprehension tasks w…