Papers by Marcus Ma
Humans Hallucinate Too: Language Models Identify and Correct Subjective Annotation Errors With Label-in-a-Haystack Prompts (2025.emnlp-main)
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Georgios Chochlakis, Peter Wu, Tikka Arjun Singh Bedi, Marcus Ma, Kristina Lerman, Shrikanth Narayanan
| Challenge: | Existing approaches to model complex subjective tasks in natural language are limited by significant variation in annotations. |
| Approach: | They propose a simple in-context learning binary filtering baseline that estimates the reasonableness of a document-label pair. |
| Outcome: | The proposed approach can be integrated into annotation pipelines to enhance signal-to-noise ratios. |
A Gentle Introduction to Deep Nets and Opportunities for the Future (2022.acl-tutorials)
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| Challenge: | a tutorial on deep nets will introduce a new language for fine tuning deep net programs . the tutorial will be divided into two parts: Part A will make deep net programming accessible to a broader audience . |
| Approach: | This tutorial introduces a new language for fine tuning deep nets with short (1-line) programs that are as easy to code as regression in statistics packages such as R. |
| Outcome: | This tutorial will introduce gft (general fine tuning), a new language for deep nets . glm is a "little language" similar to gslm in statistics package R . |
Large Language Models Do Multi-Label Classification Differently (2025.emnlp-main)
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| Challenge: | Multi-label classification is prevalent in real-world settings, but the behavior of Large Language Models (LLMs) in this setting is understudied. |
| Approach: | They propose to use initial probability distributions to analyze output distributions of LLMs at each label generation step to find out how LLM models perform multi-label classification. |
| Outcome: | The proposed methods improve alignment and predictive performance over existing methods. |