23 citations · 51 across the 13 of their papers we have counts for
14 papers · 1 filter
HalluWorld: A Controlled Benchmark for Hallucination via Reference World Models
Emmy Liu, Varun Gangal, Michael Yu +4
Hallucination remains a central failure mode of large language models, but existing benchmarks operationalize it inconsistently across summarization, question answering, retrieval-…
To Memorize or to Retrieve: Scaling the Interaction Between Pretraining and Retrieval
Karan Singh, Michael Yu, Varun Gangal +4
Retrieval-augmented generation (RAG) improves language model (LM) performance by providing relevant context at test time for knowledge-intensive situations. In this work, we system…
A Unified Definition of Hallucination: It's The World Model, Stupid!
Emmy Liu, Varun Gangal, Chelsea Zou +7
Despite numerous attempts at mitigation since the inception of language models, hallucinations remain a persistent problem even in today's frontier LLMs. Why is this? We review exi…
Maximize Your Data's Potential: Enhancing LLM Accuracy with Two-Phase Pretraining
Steven Feng, Shrimai Prabhumoye, Kezhi Kong +4
Pretraining large language models effectively requires strategic data selection, blending and ordering. However, key details about data mixtures especially their scalability to lon…
CHARD: Clinical Health-Aware Reasoning Across Dimensions for Text Generation Models
Steven Y. Feng, Vivek Khetan, Bogdan Sacaleanu +2
We motivate and introduce CHARD: Clinical Health-Aware Reasoning across Dimensions, to investigate the capability of text generation models to act as implicit clinical knowledge ba…
PINEAPPLE: Personifying INanimate Entities by Acquiring Parallel Personification data for Learning Enhanced generation
Sedrick Scott Keh, Kevin Lu, Varun Gangal +4
A personification is a figure of speech that endows inanimate entities with properties and actions typically seen as requiring animacy. In this paper, we explore the task of person…
PANCETTA: Phoneme Aware Neural Completion to Elicit Tongue Twisters Automatically
Sedrick Scott Keh, Steven Y. Feng, Varun Gangal +2
Tongue twisters are meaningful sentences that are difficult to pronounce. The process of automatically generating tongue twisters is challenging since the generated utterance must…
Retrieve, Caption, Generate: Visual Grounding for Enhancing Commonsense in Text Generation Models
Steven Y. Feng, Kevin Lu, Zhuofu Tao +4
We investigate the use of multimodal information contained in images as an effective method for enhancing the commonsense of Transformer models for text generation. We perform expe…
SAPPHIRE: Approaches for Enhanced Concept-to-Text Generation
Steven Y. Feng, Jessica Huynh, Chaitanya Narisetty +2
We motivate and propose a suite of simple but effective improvements for concept-to-text generation called SAPPHIRE: Set Augmentation and Post-hoc PHrase Infilling and REcombinatio…
A Survey of Data Augmentation Approaches for NLP
Steven Y. Feng, Varun Gangal, Jason Wei +4
Data augmentation has recently seen increased interest in NLP due to more work in low-resource domains, new tasks, and the popularity of large-scale neural networks that require la…
NAREOR: The Narrative Reordering Problem
Varun Gangal, Steven Y. Feng, Malihe Alikhani +2
Many implicit inferences exist in text depending on how it is structured that can critically impact the text's interpretation and meaning. One such structural aspect present in tex…
GenAug: Data Augmentation for Finetuning Text Generators
Steven Y. Feng, Varun Gangal, Dongyeop Kang +2
In this paper, we investigate data augmentation for text generation, which we call GenAug. Text generation and language modeling are important tasks within natural language process…
ALOHA: Artificial Learning of Human Attributes for Dialogue Agents
Aaron W. Li, Veronica Jiang, Steven Y. Feng +3
For conversational AI and virtual assistants to communicate with humans in a realistic way, they must exhibit human characteristics such as expression of emotion and personality. C…
Keep Calm and Switch On! Preserving Sentiment and Fluency in Semantic Text Exchange
Steven Y. Feng, Aaron W. Li, Jesse Hoey
In this paper, we present a novel method for measurably adjusting the semantics of text while preserving its sentiment and fluency, a task we call semantic text exchange. This is u…