Papers by Christopher Malon
Retrieval, Analogy, and Composition: A framework for Compositional Generalization in Image Captioning (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches fail to generalize well to concepts that are not observed during training. |
| Approach: | They propose a framework that revolves around probing several similar image caption training instances and performing analogical reasoning over relevant entities in retrieved prototypes. |
| Outcome: | The proposed framework improves on the widely used image captioning benchmarks and on composition-related evaluation metrics. |
On Synthesizing Data for Context Attribution in Question Answering (2025.acl-long)
Copied to clipboard
Gorjan Radevski, Kiril Gashteovski, Shahbaz Syed, Christopher Malon, Sebastien Nicolas, Chia-Chien Hung, Timo Sztyler, Verena Heußer, Wiem Ben Rim, Masafumi Enomoto, Kunihiro Takeoka, Masafumi Oyamada, Goran Glavaš, Carolin Lawrence
| Challenge: | Large Language Models (LLMs) have a tendency to hallucinate, resulting in false or misleading answers. |
| Approach: | They propose a novel generative strategy for synthesizing context attribution data. |
| Outcome: | The proposed approach is highly effective for fine-tuning small LMs for context attribution in different QA tasks and domains. |
Overcoming Poor Word Embeddings with Word Definitions (2021.starsem-1)
Copied to clipboard
| Challenge: | Modern natural language understanding models depend on pretrained word embeddings, but applications may need to reason about words that were never or rarely seen during pretraining. |
| Approach: | They propose a method to improve a model's ability to learn to use definitions in natural text to overcome this handicap. |
| Outcome: | The proposed model learns to use definitions in natural text to overcome this handicap. |
Exploring the Role of Reasoning Structures for Constructing Proofs in Multi-Step Natural Language Reasoning with Large Language Models (2024.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) are essential for performing complex multi-step reasoning tasks, such as multi-hop reasoning tasks. |
| Approach: | They propose to use large language models to derive structured intermediate proof steps to improve their performance by using examples. |
| Outcome: | The proposed models can derive correct proof steps with in-context learning. |
Improving Disentangled Text Representation Learning with Information-Theoretic Guidance (2020.acl-main)
Copied to clipboard
Pengyu Cheng, Martin Renqiang Min, Dinghan Shen, Christopher Malon, Yizhe Zhang, Yitong Li, Lawrence Carin
| Challenge: | Disentangled representation learning (DRL) maps different aspects of data into distinct and independent low-dimensional latent vector spaces. |
| Approach: | They propose a method that manifests disentangled representations of text without supervision on semantics by minimizing the upper bound between style and content. |
| Outcome: | The proposed method improves on conditional text generation and text-style transfer tasks and improves style preservation. |
KGxBoard: Explainable and Interactive Leaderboard for Evaluation of Knowledge Graph Completion Models (2022.emnlp-demos)
Copied to clipboard
Haris Widjaja, Kiril Gashteovski, Wiem Ben Rim, Pengfei Liu, Christopher Malon, Daniel Ruffinelli, Carolin Lawrence, Graham Neubig
| Challenge: | Knowledge Graphs (KGs) store information in the form of (head, predicate, tail)-triples. |
| Approach: | They propose a framework for performing fine-grained evaluation on meaningful subsets of data. |
| Outcome: | The proposed framework tests models on meaningful subsets of the data, which would have been impossible to detect with standard averaged single-score metrics. |