1 citations · 4 across the 14 of their papers we have counts for
16 papers
Explore Separately, Adopt Selectively: Execution-Guided Tool Use over Large Tool Libraries
Anna Mazhar, Seiji Maekawa, Nikita Bhutani
Enterprise LLM agents using growing tool catalogs must surface capabilities as workflows evolve without flooding context or committing to the wrong tool. Adaptive retrieval can exp…
ARCS: Towards Precise Text-to-SQL via Structured Disambiguation
Yihao Hu, Yanlin Feng, Naoki Otani +1
As text-to-SQL systems move beyond demonstrations toward real-world deployment, ambiguity in user questions becomes a primary source of errors. Such ambiguities are often subtle, d…
Do Agents Need to Plan Step-by-Step? Rethinking Planning Horizon in Data-Centric Tool Calling
Naoki Otani, Nikita Bhutani, Hannah Kim +2
Explicit planning is a critical capability for LLM-based agents solving complex data-centric tasks, which require precise tool calling over external data sources. Existing strategi…
Blue Data Intelligence Layer: Streaming Data and Agents for Multi-source Multi-modal Data-Centric Applications
Moin Aminnaseri, Farima Fatahi Bayat, Nikita Bhutani +17
NL2SQL systems aim to address the growing need for natural language interaction with data. However, real-world information rarely maps to a single SQL query because (1) users expre…
From Textual Columns to Query Plans: A Unified Relational-Semantic Execution Framework for Hybrid Query Processing
Nima Shahbazi, Seiji Maekawa, Nikita Bhutani +1
Real-world table question answering often involves hybrid schemas in which some query-relevant information is explicit in relational columns, while other attributes, predicates, or…
The Rarity Blind Spot: A Framework for Evaluating Statistical Reasoning in LLMs
Seiji Maekawa, Hayate Iso, Nikita Bhutani
Effective decision-making often relies on identifying what makes each candidate distinctive. While existing benchmarks for LLMs emphasize retrieving or summarizing information rele…
Same Content, Different Representations: A Controlled Study for Table QA
Yue Zhang, Seiji Maekawa, Nikita Bhutani
Table Question Answering (Table QA) in real-world settings must operate over both structured databases and semi-structured tables containing textual fields. However, existing bench…
Efficient Context Selection for Long-Context QA: No Tuning, No Iteration, Just Adaptive-
Chihiro Taguchi, Seiji Maekawa, Nikita Bhutani
Retrieval-augmented generation (RAG) and long-context language models (LCLMs) both address context limitations of LLMs in open-domain question answering (QA). However, optimal exte…
Orchestrating Agents and Data for Enterprise: A Blueprint Architecture for Compound AI
Eser Kandogan, Nikita Bhutani, Dan Zhang +3
Large language models (LLMs) have gained significant interest in industry due to their impressive capabilities across a wide range of tasks. However, the widespread adoption of LLM…
Evaluating Bias in LLMs for Job-Resume Matching: Gender, Race, and Education
Hayate Iso, Pouya Pezeshkpour, Nikita Bhutani +1
Large Language Models (LLMs) offer the potential to automate hiring by matching job descriptions with candidate resumes, streamlining recruitment processes, and reducing operationa…
Snippet-based Conversational Recommender System
Haibo Sun, Naoki Otani, Hannah Kim +2
Conversational Recommender Systems (CRS) engage users in interactive dialogues to gather preferences and provide personalized recommendations. While existing studies have advanced…
Natural Language Processing for Human Resources: A Survey
Naoki Otani, Nikita Bhutani, Estevam Hruschka
Advances in Natural Language Processing (NLP) have the potential to transform HR processes, from recruitment to employee management. While recent breakthroughs in NLP have generate…
From Single to Multi: How LLMs Hallucinate in Multi-Document Summarization
Catarina G. Belem, Pouya Pezeshkpour, Hayate Iso +3
Although many studies have investigated and reduced hallucinations in large language models (LLMs) for single-document tasks, research on hallucination in multi-document summarizat…
Holistic Reasoning with Long-Context LMs: A Benchmark for Database Operations on Massive Textual Data
Seiji Maekawa, Hayate Iso, Nikita Bhutani
The rapid increase in textual information means we need more efficient methods to sift through, organize, and understand it all. While retrieval-augmented generation (RAG) models e…
A Blueprint Architecture of Compound AI Systems for Enterprise
Eser Kandogan, Sajjadur Rahman, Nikita Bhutani +11
Large Language Models (LLMs) have showcased remarkable capabilities surpassing conventional NLP challenges, creating opportunities for use in production use cases. Towards this goa…
Retrieval Helps or Hurts? A Deeper Dive into the Efficacy of Retrieval Augmentation to Language Models
Seiji Maekawa, Hayate Iso, Sairam Gurajada +1
While large language models (LMs) demonstrate remarkable performance, they encounter challenges in providing accurate responses when queried for information beyond their pre-traine…