Papers by Jack Merullo
Characterizing Mechanisms for Factual Recall in Language Models (2023.emnlp-main)
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| Challenge: | Language Models often integrate facts they memorized with new information that appears in a given context, causing competition within the model. |
| Approach: | They investigate distributional and mechanistic determinants of LM behavior in a dataset that queries for knowledge of world capitals . they use head attribution to identify individual attention heads that either promote the memorized answer or the in-context answer in the logits . |
| Outcome: | The proposed method can increase the rate of generating the in-context answer to 88% of the time by scaling up or down the value vector of individual attention heads at runtime. |
ezCoref: Towards Unifying Annotation Guidelines for Coreference Resolution (2023.findings-eacl)
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Ankita Gupta, Marzena Karpinska, Wenlong Zhao, Kalpesh Krishna, Jack Merullo, Luke Yeh, Mohit Iyyer, Brendan O’Connor
| Challenge: | Existing datasets vary in definition of coreferences and are curated for linguistic experts. |
| Approach: | They propose to use ezCoref to create a crowdsourcing-friendly coreference annotation methodology that teaches annotators only cases that are treated similarly across existing datasets. |
| Outcome: | The proposed method reannotates 240 passages from seven existing english coreference datasets while teaching annotators only cases that are treated similarly across them. |
Investigating Sports Commentator Bias within a Large Corpus of American Football Broadcasts (D19-1)
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| Challenge: | a recent study shows that sports broadcasters build drama into play-by-play commentary by building team and player narratives through subjective analyses and anecdotes. |
| Approach: | They use FOOTBALL to examine racial bias in sports commentary . they identify major confounding factors for researchers examining rraecial bias . |
| Outcome: | The proposed dataset supports previous social science studies on commentator bias . it contains 1,455 broadcast football transcripts annotated with 250K player mentions and racial metadata . |
Pretraining on Interactions for Learning Grounded Affordance Representations (2022.starsem-1)
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| Challenge: | Existing studies of affordances have not integrated into formal semantics. |
| Approach: | They propose to integrate 3D objects' trajectories into a neural network to predict their traversories. |
| Outcome: | The proposed model outperforms 2D computer vision models and is more accurate than expected. |
Does CLIP Bind Concepts? Probing Compositionality in Large Image Models (2024.findings-eacl)
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| Challenge: | Large-scale neural network models combining text and images have made incredible progress in recent years, but to what extent they encode compositional representations of the concepts over which they operate remains an open question . |
| Approach: | They compare the performance of a large pretrained vision and language model (CLIP) to a set of three synthetic datasets designed to test concept binding. |
| Outcome: | The proposed model can encode compositional concepts and bind variables in a structure-sensitive way, e.g., differentiating ‘cube behind sphere’ from ‘cub behind cube’. |
Language Models Implement Simple Word2Vec-style Vector Arithmetic (2024.naacl-long)
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| Challenge: | a primary criticism of language models is their inscrutability. |
| Approach: | They propose to use a vector arithmetic style mechanism to solve relational tasks . they find that this mechanism is specific to tasks that require retrieval from pretraining memory . |
| Outcome: | The proposed model reduces to a simple additive update for a variety of tasks . the findings contribute to proving that the models are interpretable and reliable . |