← Search

Arpit Gupta

7 accepted papers

2025

FLAMES: Improving LLM Math Reasoning via a Fine-Grained Analysis of the Data Synthesis Pipeline

EMNLP 2025

Recent works improving LLM math reasoning with synthetic data have used unique setups, making comparison of data synthesis strategies impractical. This leaves many unanswered questions about the roles of different factors in the synthetic data pipeline, such as the impact of filtering low-quality pr

2024

DiNADO: Norm-Disentangled Neurally-Decomposed Oracles for Controlling Language Models

ICML 2024poster

NeurAlly-Decomposed Oracle (NADO) is a powerful approach for controllable generation with large language models. It is designed to avoid catastrophic forgetting while achieving guaranteed convergence to an entropy-maximized closed-form optimal solution with reasonable modeling capacity. Despite the…

2024

Mitigating Bias for Question Answering Models by Tracking Bias Influence

NAACL 2024long

Models of various NLP tasks have been shown to exhibit stereotypes, and the bias in the question answering (QA) models is especially harmful as the output answers might be directly consumed by the end users. There have been datasets to evaluate bias in QA models, while bias mitigation technique for…

Cited by 7SourcePDFScholar
2024

NetworkGym: Reinforcement Learning Environments for Multi-Access Traffic Management in Network Simulation

NeurIPS 2024poster

Mobile devices such as smartphones, laptops, and tablets can often connect to multiple access networks (e.g., Wi-Fi, LTE, and 5G) simultaneously. Recent advancements facilitate seamless integration of these connections below the transport layer, enhancing the experience for apps that lack inherent m…

2024

Prompting Vision-Language Models For Aspect-Controlled Generation of Referring Expressions

NAACL 2024findings

Referring Expression Generation (REG) is the task of generating a description that unambiguously identifies a given target in the scene. Different from Image Captioning (IC), REG requires learning fine-grained characteristics of not only the scene objects but also their surrounding context. Referrin…

Cited by 0SourcePDFScholar
2022

GRAVL-BERT: Graphical Visual-Linguistic Representations for Multimodal Coreference Resolution

COLING 2022main

Learning from multimodal data has become a popular research topic in recent years. Multimodal coreference resolution (MCR) is an important task in this area. MCR involves resolving the references across different modalities, e.g., text and images, which is a crucial capability for building next-gene…