Papers by Chris Thomas
SteerVLM: Robust Model Control through Lightweight Activation Steering for Vision Language Models (2025.findings-emnlp)
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| Challenge: | SteerVLM is a lightweight steering module designed to guide Vision-Language Models (VLMs) towards outputs that better adhere to desired instructions. |
| Approach: | They propose a lightweight steering module that learns from latent embeddings of paired prompts encoding target and converse behaviors to dynamically adjust activations connecting the language modality with image context. |
| Outcome: | The proposed steering module outperforms existing intervention techniques on steering and hallucination mitigation benchmarks for VLMs. |
BioDEX: Large-Scale Biomedical Adverse Drug Event Extraction for Real-World Pharmacovigilance (2023.findings-emnlp)
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Karel D’Oosterlinck, François Remy, Johannes Deleu, Thomas Demeester, Chris Develder, Klim Zaporojets, Aneiss Ghodsi, Simon Ellershaw, Jack Collins, Christopher Potts
| Challenge: | pharmacovigilance (PV) is a tool for analyzing adverse drug events from biomedical literature . pharmacologists use natural language processing to extract core information from papers . |
| Approach: | They propose a resource for biomedical adverse drug event eXtraction using natural language processing. |
| Outcome: | The proposed model achieves 59.1% F1 (validation) and estimates human performance to be 72.0% F1 . the proposed model could be used to improve drug safety monitoring, also called pharmacovigilance, in the future. |
Recipe Instruction Semantics Corpus (RISeC): Resolving Semantic Structure and Zero Anaphora in Recipes (2020.aacl-main)
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| Challenge: | Existing approaches to understanding recipe instructions make assumptions that are domain specific. |
| Approach: | They propose a new dataset for information extraction on recipes . they avoid a priori pre-defining domain-specific predicates to recognize . instead, they focus on basic understanding of the expressed semantics . |
| Outcome: | The proposed dataset avoids a priori pre-defining domain-specific predicates to recognize . instead, it focuses on basic understanding of the expressed semantics rather than reducing them to a simplified state representation. |
Zero-Shot Fine-Grained Image Classification Using Large Vision-Language Models (2025.findings-emnlp)
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| Challenge: | Large Vision-Language Models have demonstrated impressive performance on vision-language reasoning tasks, but their potential for zero-shot fine-grained image classification remains underexplored. |
| Approach: | They propose a method that transforms zero-shot fine-grained image classification into a visual question-answering framework. |
| Outcome: | The proposed method outperforms the current state-of-the-art approach and outperformed existing methods. |
Injecting Knowledge Base Information into End-to-End Joint Entity and Relation Extraction and Coreference Resolution (2021.findings-acl)
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| Challenge: | Using unsupervised entity linking, we solve named entity recognition, coreference resolution and relation extraction tasks together. |
| Approach: | They propose to use a knowledge base to inject information into a joint IE model by using unsupervised entity linking. |
| Outcome: | The proposed model improves on two datasets with 5% F1 score. |
Maximal Matching Matters: Preventing Representation Collapse for Robust Cross-Modal Retrieval (2025.acl-long)
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| Challenge: | Existing approaches to cross-modal image-text retrieval struggle with nuanced cross-modal relationships. |
| Approach: | They propose a set-based approach that represents each sample with multiple embeddings to capture nuanced and diverse relationships. |
| Outcome: | The proposed method achieves state-of-the-art performance on MS-COCO and Flickr30k without external data. |
Recognizing Multimodal Entailment (2021.acl-tutorials)
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Cesar Ilharco, Afsaneh Shirazi, Arjun Gopalan, Arsha Nagrani, Blaz Bratanic, Chris Bregler, Christina Funk, Felipe Ferreira, Gabriel Barcik, Gabriel Ilharco, Georg Osang, Jannis Bulian, Jared Frank, Lucas Smaira, Qin Cao, Ricardo Marino, Roma Patel, Thomas Leung, Vaiva Imbrasaite
| Challenge: | This tutorial introduces the multimodal entailment task for detecting semantic alignments . the task requires fine-grained understanding of visual and linguistic semantics questions . |
| Approach: | This tutorial introduces the multimodal entailment task to machine learning . it introduces a dataset for recognizing multimodal alignments . |
| Outcome: | This tutorial introduces the multimodal entailment task . it can be useful for detecting semantic alignments when a single modality alone is not enough . |
Robustifying Sentiment Classification by Maximally Exploiting Few Counterfactuals (2022.emnlp-main)
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| Challenge: | a recent study found that finetuned language models rely on spurious patterns in training data . this limitation limits their performance on out-of-distribution (OOD) test data. |
| Approach: | They propose a method that only requires annotation of a small fraction of training data . they add 1% manual counterfactuals to training data and generate extra counterfacts in vector space . |
| Outcome: | The proposed approach improves sentiment classification using IMDb data and other sets for OOD tests. |
Adversarial training for multi-context joint entity and relation extraction (D18-1)
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| Challenge: | Existing models that use adversarial training (AT) have been used in various tasks such as parsing, POS tagging, relation extraction and translation. |
| Approach: | They propose to use adversarial training (AT) to regularize neural network methods by adding small perturbations to the input data. |
| Outcome: | The proposed model improves state-of-the-art on news, biomedical, and real estate datasets and for different languages. |
M3D: MultiModal MultiDocument Fine-Grained Inconsistency Detection (2024.emnlp-main)
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| Challenge: | Existing methods for fact-checking claim are limited by ambiguous information and lack sample-level predictions. |
| Approach: | They propose a method that predicts the logical relationship of each aspect of a claim from a set of multimodal documents. |
| Outcome: | The proposed method outperforms existing models on two benchmarks while providing finer-grained predictions, explanations, and evidence. |
Diverse Content Selection for Educational Question Generation (2023.eacl-srw)
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| Challenge: | Current automatic Question Generation (QG) systems do not consider content selection as an educational aspect. |
| Approach: | They propose to select content based on relevance and topic diversity for question generation on educational document level. |
| Outcome: | The proposed solution reduces the time and effort required to create questions for students on educational datasets. |
Flexible-length Text Infilling for Discrete Diffusion Models (2025.emnlp-main)
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| Challenge: | Existing discrete diffusion models lack flexibility for text infilling without ground-truth positional data. |
| Approach: | They propose a discrete diffusion model that jointly denoises token values and token positions using a novel sample-level Optimal Transport coupling. |
| Outcome: | The proposed method outperforms existing methods on infilling benchmarks such as One-Billion-Word and Yelp. |
A Simple Geometric Method for Cross-Lingual Linguistic Transformations with Pre-trained Autoencoders (2021.emnlp-main)
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| Challenge: | Existing studies have used probing tasks to verify the presence of linguistic properties in vector representations, but it is unclear whether they can be manipulated to indirectly steer them. |
| Approach: | They validate a geometric mapping technique to transform linguistic properties without tuning . they use a pre-trained multilingual autoencoder to transform three linguistic property . |
| Outcome: | The proposed method can be used without tuning of the pre-trained autoencoder . the results are validated in monolingual and cross-lingual settings . |
MetaSumPerceiver: Multimodal Multi-Document Evidence Summarization for Fact-Checking (2024.acl-long)
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| Challenge: | Fact-checking claims on social media platforms poses a significant challenge due to the large volume of new claims constantly being posted without sufficient methods for verification. |
| Approach: | They propose a model that generates claim-specific summaries from multimodal multi-document datasets using a perceiver-based model that is able to handle inputs from multiple modalities of arbitrary lengths. |
| Outcome: | The proposed model outperforms the SOTA approach by 4.6% in the claim verification task on the MOCHEG dataset and shows strong performance on the new multi-document claims dataset. |
SoundBreak: A Systematic Study of Audio-Only Adversarial Attacks on Trimodal Models (2026.acl-long)
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| Challenge: | Recent advances in multimodal large language models have increased their vulnerability to adversarial manipulation. |
| Approach: | They propose to target audio-only adversarial attacks on multimodal audio–video–language models . they show that attacks can be successful at low perceptual distortions . |
| Outcome: | The proposed models achieve up to 96% success rate under realistic conditions . the proposed models are more robust to noise than to noise and distortion than to speech recognition systems . |
A Million Tweets Are Worth a Few Points: Tuning Transformers for Customer Service Tasks (2021.naacl-main)
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| Challenge: | In domain-specific customer service applications, many companies struggle to deploy advanced NLP models due to the limited availability of and noise in their datasets. |
| Approach: | They analyze customer service conversations on a multilingual social media corpus and compare different approaches to pretraining and finetuning on different end tasks. |
| Outcome: | The proposed model improves performance on multilingual social media data, especially in non-English settings. |
Towards Consistent Document-level Entity Linking: Joint Models for Entity Linking and Coreference Resolution (2022.acl-short)
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| Challenge: | Existing approaches to solve entity linking (EL) jointly with coreference resolution (coref) a coreferenced cluster can only be linked to a single entity or NIL (i.e., a nonlinkable entity) |
| Approach: | They propose to join entity linking and coreference resolution in a single structured prediction task over directed trees and use a globally normalized model to solve it. |
| Outcome: | The proposed model improves on two datasets with a +5% boost in accuracy compared to standalone models . the proposed model is based on current models that predict a single antecedent for each span to resolve . |
Benchmarking and Mitigating MCQA Selection Bias of Large Vision-Language Models (2025.emnlp-main)
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| Challenge: | Existing work has explored unimodal biases in visual question answering, but the problem of selection bias in Multiple-Choice Question Answering (MCQA) remains underexplored. |
| Approach: | They propose a method that mitigates bias without retraining and is compatible with frozen LVLMs. |
| Outcome: | The proposed method mitigates bias without retraining and is compatible with frozen LVLMs. |
Sub-event detection from twitter streams as a sequence labeling problem (N19-1)
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| Challenge: | Existing methods for sub-event detection do not account for sequential nature of social media streams. |
| Approach: | They propose to use a neural sequence architecture that explicitly accounts for the chronological order of posts to improve sub-event detection. |
| Outcome: | The proposed method outperforms a graph-based state-of-the-art method for binary sub-event detection (2.7% micro-F1 improvement) it also outperformed a recurrent neural network model on the posts sequence level for labeled sub- events (2.4% bin-level improvement). |