Papers by Christopher Davis
Benchmarking Deflection and Hallucination in Large Vision-Language Models (2026.acl-long)
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| Challenge: | Existing benchmarks overlook conflicts between visual and textual evidence and the importance of generating deflections when incomplete knowledge is retrieved. |
| Approach: | They propose a dynamic curation pipeline that preserves benchmark difficulty over time . they propose 'vlm-DeflectionBench' benchmark to probe model behaviour under conflicting evidence . |
| Outcome: | The proposed benchmarks overlook conflicts between visual and textual evidence and are prone to obsolescence . the proposed benchmark is based on 2,775 samples spanning diverse retrieval settings . |
Prompting open-source and commercial language models for grammatical error correction of English learner text (2024.findings-acl)
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Christopher Davis, Andrew Caines, O Andersen, Shiva Taslimipoor, Helen Yannakoudakis, Zheng Yuan, Christopher Bryant, Marek Rei, Paula Buttery
| Challenge: | Recent advances in generative AI have enabled us to prompt large language models (LLMs) to produce texts which are fluent and grammatical. |
| Approach: | They evaluate model performance by measuring their performance on established benchmarks. |
| Outcome: | The proposed models outperform supervised English GEC models on fluency correction benchmarks and commercial LLMs on edit benchmarks. |
GaRAGe: A Benchmark with Grounding Annotations for RAG Evaluation (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have shown consistent improvements across many tasks requiring natural language understanding, coding, mathematical or logical reasoning . |
| Approach: | They propose to use GaRAGe to evaluate whether LLMs can identify relevant grounding when generating RAG answers. |
| Outcome: | The proposed model over-summarises rather than ground answers strictly on annotated relevant passages, or deflects when no relevant grounding is available. |
The Indigenous Languages Technology project at NRC Canada: An empowerment-oriented approach to developing language software (2020.coling-main)
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Roland Kuhn, Fineen Davis, Alain Désilets, Eric Joanis, Anna Kazantseva, Rebecca Knowles, Patrick Littell, Delaney Lothian, Aidan Pine, Caroline Running Wolf, Eddie Santos, Darlene Stewart, Gilles Boulianne, Vishwa Gupta, Brian Maracle Owennatékha, Akwiratékha’ Martin, Christopher Cox, Marie-Odile Junker, Olivia Sammons, Delasie Torkornoo, Nathan Thanyehténhas Brinklow, Sara Child, Benoît Farley, David Huggins-Daines, Daisy Rosenblum, Heather Souter
| Challenge: | This paper describes the first, three-year phase of a project at the National Research Council of Canada that is developing software to assist Indigenous communities in preserving their languages and extending their use. |
| Approach: | They describe the first phase of a project at the National Research Council of Canada that is developing software to assist Indigenous communities in preserving their languages. |
| Outcome: | The proposed software will help Indigenous communities preserve and revitalize their languages and extend their use. |
Multi-Class Grammatical Error Detection for Correction: A Tale of Two Systems (2021.emnlp-main)
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| Challenge: | a multi-class grammatical error detection system can be used to improve grammamatical errors correction (GEC) for English. |
| Approach: | They develop a multi-class grammatical error detection system based on pre-trained ELECTRA and extend it to multi-Class detection using different error type tagsets. |
| Outcome: | The proposed system outperforms previous systems on the BEA-test benchmark. |