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Shubhashis Roy Dipta

ACL 2026×3NeurIPS 2026×2强化学习与后训练多模态评测与基准智能体与工具对齐与偏好
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2 一作 · 5 篇 · ACL 2026 / NeurIPS 2026

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2026 顶会论文

5
一作ACL 2026Poster· Natural Language Processing

VC -Inspector: Advancing Reference-free Evaluation of Video Captions with Factual Analysis

Shubhashis Roy Dipta · Tz-Ying Wu · Subarna Tripathi

摘要

We propose VC-Inspector, a lightweight, open-source large multimodal model (LMM) for reference-free evaluation of video captions, with a focus on factual accuracy. Unlike existing metrics that suffer from limited context handling, weak factuality assessment, or reliance on proprietary services, V...

多模态评测与基准对齐与偏好OpenReview 项目页
作者位次 #2/4ACL 2026Findings· Findings of ACL

Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks

Nobin Sarwar · Shubhashis Roy Dipta · Zheyuan Liu · Vaidehi Patil

摘要

With the growing adoption of VLMs, DMs, LLMs, and AFMs, these multimodal foundation models can inadvertently encode sensitive, copyrighted, biased, or unsafe cross-modal associations that originate from their training data. Retraining after deletion requests or policy updates is often impractical...

多模态检索与知识安全OpenReview 项目页
一作ACL 2026Findings· Findings of ACL

G anit LLM : Difficulty-Aware B engali Mathematical Reasoning through Curriculum- GRPO

Shubhashis Roy Dipta · Khairul Mahbub · Nadia Najjar

摘要

We present a Bengali mathematical reasoning model called GanitLLM (named after the Bangla word for mathematics, “Ganit”), together with a new difficulty-aware Bengali math corpus and a curriculum-based GRPO pipeline. Bengali is one of the world’s most widely spoken languages, yet existing LLMs ei...

强化学习与后训练推理评测与基准OpenReview 项目页

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