Papers by Mayukh Das

2 papers
Quantifying Bias from Decoding Techniques in Natural Language Generation (2022.coling-1)

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Challenge: Natural language generation (NLG) models can propagate social bias towards particular demography.
Approach: They propose to examine whether bias metrics like toxicity and sentiment are impacted by decoding techniques that use stochastic decoding.
Outcome: The proposed methods reveal the imperative of testing inference time bias and provide evidence on the usefulness of inspecting the entire decoding spectrum.
Learning Optimal Message Representations for Agentic Communication (2026.findings-acl)

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Challenge: Existing approaches lack the intelligence necessary to understand, learn or apply optimal communication representations adaptively.
Approach: They propose to dynamically learn the optimal message representations to enhance agentic performance by using an Expanding Markov Decision Process.
Outcome: The proposed framework improves agentic performance while maintaining efficiency.

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