4 citations · 11 across the 16 of their papers we have counts for
11 papers · 1 filter
ClusterFusion: Hybrid Clustering with Embedding Guidance and LLM Adaptation
Yiming Xu, Yuan Yuan, Vijay Viswanathan +1
Text clustering is a fundamental task in natural language processing, yet traditional clustering algorithms with pre-trained embeddings often struggle in domain-specific contexts w…
Checklists Are Better Than Reward Models For Aligning Language Models
Vijay Viswanathan, Yanchao Sun, Shuang Ma +4
Language models must be adapted to understand and follow user instructions. Reinforcement learning is widely used to facilitate this -- typically using fixed criteria such as "help…
Evaluating Language Models as Synthetic Data Generators
Seungone Kim, Juyoung Suk, Xiang Yue +7
Given the increasing use of synthetic data in language model (LM) post-training, an LM's ability to generate high-quality data has become nearly as crucial as its ability to solve…
SELF-GUIDE: Better Task-Specific Instruction Following via Self-Synthetic Finetuning
Chenyang Zhao, Xueying Jia, Vijay Viswanathan +2
Large language models (LLMs) hold the promise of solving diverse tasks when provided with appropriate natural language prompts. However, prompting often leads models to make predic…
Training Task Experts through Retrieval Based Distillation
Jiaxin Ge, Xueying Jia, Vijay Viswanathan +2
One of the most reliable ways to create deployable models for specialized tasks is to obtain an adequate amount of high-quality task-specific data. However, for specialized tasks,…
Synthetic Multimodal Question Generation
Ian Wu, Sravan Jayanthi, Vijay Viswanathan +4
Multimodal Retrieval Augmented Generation (MMRAG) is a powerful approach to question-answering over multimodal documents. A key challenge with evaluating MMRAG is the paucity of hi…
Better Synthetic Data by Retrieving and Transforming Existing Datasets
Saumya Gandhi, Ritu Gala, Vijay Viswanathan +2
Despite recent advances in large language models, building dependable and deployable NLP models typically requires abundant, high-quality training data. However, task-specific data…
Prompt2Model: Generating Deployable Models from Natural Language Instructions
Vijay Viswanathan, Chenyang Zhao, Amanda Bertsch +2
Large language models (LLMs) enable system builders today to create competent NLP systems through prompting, where they only need to describe the task in natural language and provi…
Large Language Models Enable Few-Shot Clustering
Vijay Viswanathan, Kiril Gashteovski, Carolin Lawrence +2
Unlike traditional unsupervised clustering, semi-supervised clustering allows users to provide meaningful structure to the data, which helps the clustering algorithm to match the u…
A Dataset for N-ary Relation Extraction of Drug Combinations
Aryeh Tiktinsky, Vijay Viswanathan, Danna Niezni +5
Combination therapies have become the standard of care for diseases such as cancer, tuberculosis, malaria and HIV. However, the combinatorial set of available multi-drug treatments…
Rethinking End-to-End Evaluation of Decomposable Tasks: A Case Study on Spoken Language Understanding
Siddhant Arora, Alissa Ostapenko, Vijay Viswanathan +4
Decomposable tasks are complex and comprise of a hierarchy of sub-tasks. Spoken intent prediction, for example, combines automatic speech recognition and natural language understan…