49 citations · 101 across the 24 of their papers we have counts for
25 papers
A Small Model Asks Better: Fine-Tuning Query Clarification for Retrieval-Augmented Generation
Lihong He, Lucian Popa, Marina Danilevsky
Query clarification is essential for retrieval-augmented generation (RAG) systems that face ambiguous user queries. We consider multi-turn grounded conversational scenarios where a…
MTRAG-UN: A Benchmark for Open Challenges in Multi-Turn RAG Conversations
Sara Rosenthal, Yannis Katsis, Vraj Shah +3
We present MTRAG-UN, a benchmark for exploring open challenges in multi-turn retrieval augmented generation, a popular use of large language models. We release a benchmark of 666 t…
A Longitudinal Study on Different Annotator Feedback Loops in Complex RAG Tasks
Sara Rosenthal, Maeda Hanafi, Yannis Katsis +2
Grounding conversations in existing passages, known as Retrieval-Augmented Generation (RAG), is an important aspect of Chat-Based Assistants powered by Large Language Models (LLMs)…
Activated LoRA: Fine-tuned LLMs for Intrinsics
Kristjan Greenewald, Luis Lastras, Thomas Parnell +6
Low-Rank Adaptation (LoRA) has emerged as a highly efficient framework for finetuning the weights of large foundation models, and has become the go-to method for data-driven custom…
A Library of LLM Intrinsics for Retrieval-Augmented Generation
Marina Danilevsky, Kristjan Greenewald, Chulaka Gunasekara +13
In the developer community for large language models (LLMs), there is not yet a clean pattern analogous to a software library, to support very large scale collaboration. Even for t…
MTRAG: A Multi-Turn Conversational Benchmark for Evaluating Retrieval-Augmented Generation Systems
Yannis Katsis, Sara Rosenthal, Kshitij Fadnis +7
Retrieval-augmented generation (RAG) has recently become a very popular task for Large Language Models (LLMs). Evaluating them on multi-turn RAG conversations, where the system is…
DELIFT: Data Efficient Language model Instruction Fine Tuning
Ishika Agarwal, Krishnateja Killamsetty, Lucian Popa +1
Fine-tuning large language models (LLMs) is essential for enhancing their performance on specific tasks but is often resource-intensive due to redundant or uninformative data. To a…
ConTReGen: Context-driven Tree-structured Retrieval for Open-domain Long-form Text Generation
Kashob Kumar Roy, Pritom Saha Akash, Kevin Chen-Chuan Chang +1
Open-domain long-form text generation requires generating coherent, comprehensive responses that address complex queries with both breadth and depth. This task is challenging due t…
Seed-Guided Fine-Grained Entity Typing in Science and Engineering Domains
Yu Zhang, Yunyi Zhang, Yanzhen Shen +5
Accurately typing entity mentions from text segments is a fundamental task for various natural language processing applications. Many previous approaches rely on massive human-anno…
Long-form Question Answering: An Iterative Planning-Retrieval-Generation Approach
Pritom Saha Akash, Kashob Kumar Roy, Lucian Popa +1
Long-form question answering (LFQA) poses a challenge as it involves generating detailed answers in the form of paragraphs, which go beyond simple yes/no responses or short factual…
Are Human Explanations Always Helpful? Towards Objective Evaluation of Human Natural Language Explanations
Bingsheng Yao, Prithviraj Sen, Lucian Popa +2
Human-annotated labels and explanations are critical for training explainable NLP models. However, unlike human-annotated labels whose quality is easier to calibrate (e.g., with a…
Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecture
Bingsheng Yao, Ishan Jindal, Lucian Popa +8
Real-world domain experts (e.g., doctors) rarely annotate only a decision label in their day-to-day workflow without providing explanations. Yet, existing low-resource learning tec…
A Framework for Combining Entity Resolution and Query Answering in Knowledge Bases
Ronald Fagin, Phokion G. Kolaitis, Domenico Lembo +2
We propose a new framework for combining entity resolution and query answering in knowledge bases (KBs) with tuple-generating dependencies (tgds) and equality-generating dependenci…
MILO: Model-Agnostic Subset Selection Framework for Efficient Model Training and Tuning
Krishnateja Killamsetty, Alexandre V. Evfimievski, Tejaswini Pedapati +3
Training deep networks and tuning hyperparameters on large datasets is computationally intensive. One of the primary research directions for efficient training is to reduce trainin…
Entity Set Co-Expansion in StackOverflow
Yu Zhang, Yunyi Zhang, Yucheng Jiang +5
Given a few seed entities of a certain type (e.g., Software or Programming Language), entity set expansion aims to discover an extensive set of entities that share the same type as…
AUTOMATA: Gradient Based Data Subset Selection for Compute-Efficient Hyper-parameter Tuning
Krishnateja Killamsetty, Guttu Sai Abhishek, Aakriti +4
Deep neural networks have seen great success in recent years; however, training a deep model is often challenging as its performance heavily depends on the hyper-parameters used. I…
Domain Representative Keywords Selection: A Probabilistic Approach
Pritom Saha Akash, Jie Huang, Kevin Chen-Chuan Chang +3
We propose a probabilistic approach to select a subset of a \textit{target domain representative keywords} from a candidate set, contrasting with a context domain. Such a task is c…
Learning to Robustly Aggregate Labeling Functions for Semi-supervised Data Programming
Ayush Maheshwari, Krishnateja Killamsetty, Ganesh Ramakrishnan +3
A critical bottleneck in supervised machine learning is the need for large amounts of labeled data which is expensive and time consuming to obtain. However, it has been shown that…
LNN-EL: A Neuro-Symbolic Approach to Short-text Entity Linking
Hang Jiang, Sairam Gurajada, Qiuhao Lu +5
Entity linking (EL), the task of disambiguating mentions in text by linking them to entities in a knowledge graph, is crucial for text understanding, question answering or conversa…
Leveraging Abstract Meaning Representation for Knowledge Base Question Answering
Pavan Kapanipathi, Ibrahim Abdelaziz, Srinivas Ravishankar +27
Knowledge base question answering (KBQA)is an important task in Natural Language Processing. Existing approaches face significant challenges including complex question understandin…
Learning Structured Representations of Entity Names using Active Learning and Weak Supervision
Kun Qian, Poornima Chozhiyath Raman, Yunyao Li +1
Structured representations of entity names are useful for many entity-related tasks such as entity normalization and variant generation. Learning the implicit structured representa…
Global Table Extractor (GTE): A Framework for Joint Table Identification and Cell Structure Recognition Using Visual Context
Xinyi Zheng, Doug Burdick, Lucian Popa +2
Documents are often used for knowledge sharing and preservation in business and science, within which are tables that capture most of the critical data. Unfortunately, most documen…
A Comprehensive Benchmark Framework for Active Learning Methods in Entity Matching
Venkata Vamsikrishna Meduri, Lucian Popa, Prithviraj Sen +1
Entity Matching (EM) is a core data cleaning task, aiming to identify different mentions of the same real-world entity. Active learning is one way to address the challenge of scarc…
Low-resource Deep Entity Resolution with Transfer and Active Learning
Jungo Kasai, Kun Qian, Sairam Gurajada +2
Entity resolution (ER) is the task of identifying different representations of the same real-world entities across databases. It is a key step for knowledge base creation and text…