Papers by Phillip Howard
Fusing Temporal Graphs into Transformers for Time-Sensitive Question Answering (2023.findings-emnlp)
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| Challenge: | Existing methods for extracting temporal information from text are not suitable for time-sensitive questions. |
| Approach: | They propose to use existing temporal information extraction systems to construct temporal graphs of events, times, and temporal relations in questions and documents. |
| Outcome: | The proposed method outperforms graph convolution-based approaches on SituatedQA and TimeQA. |
NeuroComparatives: Neuro-Symbolic Distillation of Comparative Knowledge (2024.findings-naacl)
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| Challenge: | Comparative knowledge is an essential component of our world knowledge, yet understudied in prior literature. |
| Approach: | They propose a framework for comparative knowledge distillation overgenerated from language models . they use a corpus of 8.8M comparisons over 1.74M entity pairs to acquire comparative information . |
| Outcome: | The proposed framework acquires comparative knowledge between everyday objects . human evaluations show that it outperforms existing resources in terms of validity . |
NeuroPrompts: An Adaptive Framework to Optimize Prompts for Text-to-Image Generation (2024.eacl-demo)
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| Challenge: | Recent advances in text-to-image diffusion models have made it difficult to obtain high-quality images. |
| Approach: | They propose an adaptive framework that automatically enhances a user's prompt to improve the quality of generation models. |
| Outcome: | The proposed framework generates prompts similar to those produced by human prompt engineers and provides user control over stylistic features via constraint set specification. |
NeuroCounterfactuals: Beyond Minimal-Edit Counterfactuals for Richer Data Augmentation (2022.findings-emnlp)
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| Challenge: | Existing approaches to produce counterfactuals rely on small perturbations via minimal edits, resulting in simplistic changes. |
| Approach: | They propose a novel approach to produce counterfactuals that allow for larger edits and linguistic diversity while still bearing similarity to the original document. |
| Outcome: | The proposed approach outperforms existing methods for generalizing natural language models under select settings. |
Uncovering Bias in Large Vision-Language Models at Scale with Counterfactuals (2025.naacl-long)
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| Challenge: | Large Vision-Language Models (LVLMs) have been proposed to augment LLMs with visual inputs. |
| Approach: | They propose large vision-Language Models to augment LLMs with visual inputs. |
| Outcome: | The proposed models condition generated text on both an input image and a visual prompt, enabling a variety of use cases such as visual question answering and multimodal chat. |
Semi-Structured Chain-of-Thought: Integrating Multiple Sources of Knowledge for Improved Language Model Reasoning (2024.naacl-long)
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| Challenge: | Existing prompting methods rely on only one or two of these sources, or require repeatedly invoking large language models to generate similar or identical content. |
| Approach: | They propose a semi-structured prompting approach that integrates parametric memory with unstructured knowledge from text documents and structured knowledge from knowledge graphs. |
| Outcome: | The proposed prompting method surpasses existing prompting methods even exceeding those that require fine-tuning on open-domain multi-hop question answering datasets. |
Transformer-Based Temporal Information Extraction and Application: A Review (2025.emnlp-main)
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| Challenge: | Temporal information extraction (IE) aims to extract structured temporal information from unstructured text, thereby uncovering the implicit timelines within. |
| Approach: | They summarize and analyze the work using Transformers to highlight potential future directions. |
| Outcome: | The proposed method is applied across healthcare, newswire, and intelligence analysis domains. |
InterpreT: An Interactive Visualization Tool for Interpreting Transformers (2021.eacl-demos)
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Vasudev Lal, Arden Ma, Estelle Aflalo, Phillip Howard, Ana Simoes, Daniel Korat, Oren Pereg, Gadi Singer, Moshe Wasserblat
| Challenge: | Using Transformer-based models for NLU/NLP tasks is a growing interest . but there are many open questions regarding the behavior of these models . |
| Approach: | They present an interactive visualization tool for interpreting Transformer-based models. |
| Outcome: | The tool can track and visualize token embeddings through each layer of a Transformer, highlight distances between certain token embeds, and identify task-related functions of attention heads using new metrics. |
Pruning the Paradox: How CLIP’s Most Informative Heads Enhance Performance While Amplifying Bias (2025.emnlp-main)
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| Challenge: | Large-scale vision-language models such as CLIP have advanced state-of-the-art performance in vision tasks . however, as they gain prominence in real-world applications, their embedded social biases can be harmful . et al., 2021: 103-104. |
| Approach: | They propose an interpretability metric that measures how consistently attention heads align with specific concepts in CLIP-like models. |
| Outcome: | The proposed interpretability metric measures how consistently attention heads align with specific concepts. |
LVLM-Compress-Bench: Benchmarking the Broader Impact of Large Vision-Language Model Compression (2025.findings-naacl)
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Souvik Kundu, Anahita Bhiwandiwalla, Sungduk Yu, Phillip Howard, Tiep Le, Sharath Nittur Sridhar, David Cobbley, Hao Kang, Vasudev Lal
| Challenge: | LVLMs have been shown to perform well on simple uni-modal benchmarks, but their detailed study on multi-modal models is still lacking. |
| Approach: | They propose a framework to analyze the impact of compression on LVLMs on multi-modal input driven tasks. |
| Outcome: | The proposed framework analyzes the impact of compression on generative performance of large vision language models on multi-modal input driven tasks. |