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Varun Gangal

7 accepted papers

2024

Enhancing Hallucination Detection through Perturbation-Based Synthetic Data Generation in System Responses

ACL 2024findings

Detecting hallucinations in large language model (LLM) outputs is pivotal, yet traditional fine-tuning for this classification task is impeded by the expensive and quickly outdated annotation process, especially across numerous vertical domains and in the face of rapid LLM advancements. In this stud…

2022

NAREOR: The Narrative Reordering Problem

AAAI 2022technical

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 text with chronology is the order of its presentation. For narratives or stories, this is known as the narrative order. Reo…

2022

PINEAPPLE: Personifying INanimate Entities by Acquiring Parallel Personification Data for Learning Enhanced Generation

COLING 2022main

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 personification generation. To this end, we propose PINEAPPLE: Personifying INanimate Entities by Acquiring Parallel Personifi…

2022

Retrieve, Caption, Generate: Visual Grounding for Enhancing Commonsense in Text Generation Models

AAAI 2022technical

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 experiments using BART and T5 on concept-to-text generation, specifically the task of generative commonsense reasoning, or C…

2021

Automatic Construction of Evaluation Suites for Natural Language Generation Datasets

NeurIPS 2021poster

Machine learning approaches applied to NLP are often evaluated by summarizing their performance in a single number, for example accuracy. Since most test sets are constructed as an i.i.d. sample from the overall data, this approach overly simplifies the complexity of language and encourages overfitt…

Cited by 20SourceScholar
2021

Coarse2Fine: Fine-grained Text Classification on Coarsely-grained Annotated Data

EMNLP 2021main

Existing text classification methods mainly focus on a fixed label set, whereas many real-world applications require extending to new fine-grained classes as the number of samples per label increases. To accommodate such requirements, we introduce a new problem called coarse-to-fine grained classifi…

2021

Investigating Robustness of Dialog Models to Popular Figurative Language Constructs

EMNLP 2021main

Humans often employ figurative language use in communication, including during interactions with dialog systems. Thus, it is important for real-world dialog systems to be able to handle popular figurative language constructs like metaphor and simile. In this work, we analyze the performance of exist…