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Chris Thomas

11 accepted papers

2026

Immunizing Models Against Harmful Long-Horizon Fine-Tuning via Contractive Optimization Dynamics

Najibul Haque Sarker, Zaber Ibn Abdul Hakim, Ali Asgarov, Chia-Wei Tang, Alvi Md Ishmam, Chris Thomas
CVPR 2026

Fine-tuning has become the default way to adapt powerful foundation models, but this also enables low-cost repurposing for harmful objectives. Existing immunization methods try to optimize local geometry or simulate short attacker horizons, and penalize observed loss drops. However, in practice, dow

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2026

LAMP: Learning Universal Adversarial Perturbations for Multi-Image Tasks via Pre-trained Models

Alvi Md Ishmam, Najibul Haque Sarker, Zaber Ibn Abdul Hakim, Chris Thomas
AAAI 2026technical

Multimodal Large Language Models (MLLMs) have achieved remarkable performance across vision-language tasks. Recent advancements allow these models to process multiple images as inputs. However, the vulnerabilities of multi-image MLLMs remain unexplored. Existing adversarial attacks focus on single-i

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2025

Benchmarking and Mitigating MCQA Selection Bias of Large Vision-Language Models

Md. Atabuzzaman, Ali Asgarov, Chris Thomas
EMNLP 2025

Large Vision-Language Models (LVLMs) have achieved strong performance on vision-language tasks, particularly Visual Question Answering (VQA). While prior work has explored unimodal biases in VQA, the problem of selection bias in Multiple-Choice Question Answering (MCQA), where models may favor speci

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2025

Flexible-length Text Infilling for Discrete Diffusion Models

Andrew Zhang, Anushka Sivakumar, Chia-Wei Tang, Chris Thomas
EMNLP 2025

Discrete diffusion models are a new class of text generators that offer advantages such as bidirectional context use, parallelizable generation, and flexible prompting compared to autoregressive models. However, a critical limitation of discrete diffusion models is their inability to perform flexibl

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2025

Maximal Matching Matters: Preventing Representation Collapse for Robust Cross-Modal Retrieval

Hani Alomari, Anushka Sivakumar, Andrew Zhang, Chris Thomas
ACL 2025long

Cross-modal image-text retrieval is challenging because of the diverse possible associations between content from different modalities. Traditional methods learn a single-vector embedding to represent semantics of each sample, but struggle to capture nuanced and diverse relationships that can exist…

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2025

SteerVLM: Robust Model Control through Lightweight Activation Steering for Vision Language Models

Anushka Sivakumar, Andrew Zhang, Zaber Ibn Abdul Hakim, Chris Thomas
EMNLP 2025

This work introduces SteerVLM, a lightweight steering module designed to guide Vision-Language Models (VLMs) towards outputs that better adhere to desired instructions. Our approach learns from the latent embeddings of paired prompts encoding target and converse behaviors to dynamically adjust activ

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2025

Zero-Shot Fine-Grained Image Classification Using Large Vision-Language Models

Md. Atabuzzaman, Andrew Zhang, Chris Thomas
EMNLP 2025

Large Vision-Language Models (LVLMs) have demonstrated impressive performance on vision-language reasoning tasks. However, their potential for zero-shot fine-grained image classification, a challenging task requiring precise differentiation between visually similar categories, remains underexplored.

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2024

JourneyBench: A Challenging One-Stop Vision-Language Understanding Benchmark of Generated Images

Zhecan Wang, Junzhang Liu, Chia-Wei Tang, Hani Alomari, Anushka Sivakumar, Rui Sun +8
NeurIPS 2024poster

Existing vision-language understanding benchmarks largely consist of images of objects in their usual contexts. As a consequence, recent multimodal large language models can perform well with only a shallow visual understanding by relying on background language biases. Thus, strong performance on th…

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2024

M3D: MultiModal MultiDocument Fine-Grained Inconsistency Detection

Chia-Wei Tang, Ting-Chih Chen, Kiet A. Nguyen, Kazi Sajeed Mehrab, Alvi Md Ishmam, Chris Thomas
EMNLP 2024main

Fact-checking claims is a highly laborious task that involves understanding how each factual assertion within the claim relates to a set of trusted source materials. Existing approaches make sample-level predictions but fail to identify the specific aspects of the claim that are troublesome and the…

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2024

MetaSumPerceiver: Multimodal Multi-Document Evidence Summarization for Fact-Checking

Ting-Chih Chen, Chia-Wei Tang, Chris Thomas
ACL 2024long

Fact-checking real-world claims often requires reviewing multiple multimodal documents in order to assess the claim’s truthfulness, a highly laborious and time-consuming task. In this paper, we present a summarization model crafted to generate claim-specific summaries useful for fact-checking from m…

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