Papers by Rakesh R Menon
DISCERN: Decoding Systematic Errors in Natural Language for Text Classifiers (2024.emnlp-main)
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| Challenge: | Recent work proposes automatic methods for identifying and explaining systematic biases using keywords. |
| Approach: | They propose automatic methods for identifying and explaining systematic biases using keywords. |
| Outcome: | The proposed framework improves classifiers by augmenting training sets with synthetically generated instances or annotated examples via active learning. |
SocialGaze: Improving the Integration of Human Social Norms in Large Language Models (2024.findings-emnlp)
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| Challenge: | Increasingly, large language models (LLMs) are able to understand and rationalize socially acceptable behaviors, but they are often misaligned with human consensus. |
| Approach: | They propose a multi-step prompting framework that verbalizes a social situation from multiple perspectives before forming a judgment. |
| Outcome: | The proposed framework improves the alignment with human judgments by up to 11 F1 points with the GPT-3.5 model. |
INTERACT: Enabling Interactive, Question-Driven Learning in Large Language Models (2025.acl-long)
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| Challenge: | Large language models (LLMs) absorb static data without the ability to question and refine knowledge. |
| Approach: | They propose a framework in which a “student” LLM engages a ‘teacher’ LLM through iterative inquiries to acquire knowledge across 1,347 contexts. |
| Outcome: | The proposed framework achieves up to 25% improvement in 1,347 contexts across a wide range of scenarios and LLM architectures, with ‘cold-start’ student models matching static learning baselines in as few as five dialogue turns. |
Explaining Differences Between Model Pairs in Natural Language through Sample Learning (2025.emnlp-main)
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| Challenge: | a framework that generates faithful natural language explanations of when and how two ML models converge or diverge in their predictions requires access to training data. |
| Approach: | They propose a framework that generates faithful natural language explanations of when and how two ML models converge or diverge in their predictions. |
| Outcome: | The proposed framework generates faithful natural language explanations of when and how two models diverge in their predictions. |