29 citations · 64 across the 17 of their papers we have counts for
18 papers
MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models
Haohang Huang, Xuan Lu, Mingyi Su +9
Multimodal embedding models aim to map heterogeneous inputs, such as text, images, videos, and audio, into a shared semantic space. However, existing methods and benchmarks remain…
VISTA: A Test-Time Self-Improving Video Generation Agent
Do Xuan Long, Xingchen Wan, Hootan Nakhost +3
Despite rapid advances in text-to-video synthesis, generated video quality remains critically dependent on precise user prompts. Existing test-time optimization methods, successful…
MLE-STAR: Machine Learning Engineering Agent via Search and Targeted Refinement
Jaehyun Nam, Jinsung Yoon, Jiefeng Chen +3
Agents based on large language models (LLMs) for machine learning engineering (MLE) can automatically implement ML models via code generation. However, existing approaches to build…
SQL-GEN: Bridging the Dialect Gap for Text-to-SQL Via Synthetic Data And Model Merging
Mohammadreza Pourreza, Ruoxi Sun, Hailong Li +3
Recent advances in Text-to-SQL have largely focused on the SQLite dialect, neglecting the diverse landscape of SQL dialects like BigQuery and PostgreSQL. This limitation is due to…
CROME: Cross-Modal Adapters for Efficient Multimodal LLM
Sayna Ebrahimi, Sercan O. Arik, Tejas Nama +1
Multimodal Large Language Models (MLLMs) demonstrate remarkable image-language capabilities, but their widespread use faces challenges in cost-effective training and adaptation. Ex…
Matryoshka-Adaptor: Unsupervised and Supervised Tuning for Smaller Embedding Dimensions
Jinsung Yoon, Raj Sinha, Sercan O Arik +1
Embeddings from Large Language Models (LLMs) have emerged as critical components in various applications, particularly for information retrieval. While high-dimensional embeddings…
Learned Feature Importance Scores for Automated Feature Engineering
Yihe Dong, Sercan Arik, Nathanael Yoder +1
Feature engineering has demonstrated substantial utility for many machine learning workflows, such as in the small data regime or when distribution shifts are severe. Thus automati…
Chain of Agents: Large Language Models Collaborating on Long-Context Tasks
Yusen Zhang, Ruoxi Sun, Yanfei Chen +3
Addressing the challenge of effectively processing long contexts has become a critical issue for Large Language Models (LLMs). Two common strategies have emerged: 1) reducing the i…
Learning to Clarify: Multi-turn Conversations with Action-Based Contrastive Self-Training
Maximillian Chen, Ruoxi Sun, Tomas Pfister +1
Large language models (LLMs), optimized through human feedback, have rapidly emerged as a leading paradigm for developing intelligent conversational assistants. However, despite th…
Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning
Sungwon Han, Jinsung Yoon, Sercan O Arik +1
Large Language Models (LLMs), with their remarkable ability to tackle challenging and unseen reasoning problems, hold immense potential for tabular learning, that is vital for many…
Mitigating Object Hallucination in MLLMs via Data-augmented Phrase-level Alignment
Pritam Sarkar, Sayna Ebrahimi, Ali Etemad +3
Despite their significant advancements, Multimodal Large Language Models (MLLMs) often generate factually inaccurate information, referred to as hallucination. In this work, we add…
Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding
Zilong Wang, Hao Zhang, Chun-Liang Li +8
Table-based reasoning with large language models (LLMs) is a promising direction to tackle many table understanding tasks, such as table-based question answering and fact verificat…
TextGenSHAP: Scalable Post-hoc Explanations in Text Generation with Long Documents
James Enouen, Hootan Nakhost, Sayna Ebrahimi +3
Large language models (LLMs) have attracted huge interest in practical applications given their increasingly accurate responses and coherent reasoning abilities. Given their nature…
SQLPrompt: In-Context Text-to-SQL with Minimal Labeled Data
Ruoxi Sun, Sercan Ö. Arik, Rajarishi Sinha +4
Text-to-SQL aims to automate the process of generating SQL queries on a database from natural language text. In this work, we propose "SQLPrompt", tailored to improve the few-shot…
Effective Large Language Model Adaptation for Improved Grounding and Citation Generation
Xi Ye, Ruoxi Sun, Sercan Ö. Arik +1
Large language models (LLMs) have achieved remarkable advancements in natural language understanding and generation. However, one major issue towards their widespread deployment in…
COSTAR: Improved Temporal Counterfactual Estimation with Self-Supervised Learning
Chuizheng Meng, Yihe Dong, Sercan Ö. Arık +2
Estimation of temporal counterfactual outcomes from observed history is crucial for decision-making in many domains such as healthcare and e-commerce, particularly when randomized…
Search-Adaptor: Embedding Customization for Information Retrieval
Jinsung Yoon, Sercan O Arik, Yanfei Chen +1
Embeddings extracted by pre-trained Large Language Models (LLMs) have significant potential to improve information retrieval and search. Beyond the zero-shot setup in which they ar…
SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL (extended)
Ruoxi Sun, Sercan Ö. Arik, Alex Muzio +8
Text-to-SQL, the process of translating natural language into Structured Query Language (SQL), represents a transformative application of large language models (LLMs), potentially…