Papers by Manoj Kumar
Unsupervised training data re-weighting for natural language understanding with local distribution approximation (2022.emnlp-industry)
Copied to clipboard
| Challenge: | a distribution mismatch between offline training and live data can cause biases . cyclic seasonality shifts, and changing pool of users can contribute to this problem . |
| Approach: | They propose an unsupervised approach to mitigate offline training data sampling bias . they propose a local distribution approximation in the pre-trained embedding space . |
| Outcome: | The proposed approach mitigates the offline training data sampling bias in multiple NLU tasks without additional annotation. |
On Localizing and Deleting Toxic Memories in Large Language Models (2025.findings-naacl)
Copied to clipboard
Anubrata Das, Manoj Kumar, Ninareh Mehrabi, Anil Ramakrishna, Anna Rumshisky, Kai-Wei Chang, Aram Galstyan, Morteza Ziyadi, Rahul Gupta
| Challenge: | Existing methods to reduce toxic generation in large language models are not fully understood. |
| Approach: | They propose to understand the mechanisms that drive toxic generation in large language models by using memory localization to reduce toxic generation. |
| Outcome: | The proposed method reduces toxic generation from 62.86% to 28.61%, but it also improves generation quality. |
Controlled Data Generation via Insertion Operations for NLU (2022.naacl-industry)
Copied to clipboard
| Challenge: | a new approach to annotate live traffic is emerging to be cost-effective and efficient . manual data annotation is expensive and not preferred for meeting customer privacy expectations . |
| Approach: | They propose a targeted synthetic data generation technique by inserting tokens into a given semantic signature. |
| Outcome: | The proposed approach achieves the same accuracy as training with all available data on a voice assistant dataset. |
Improving Large-Scale Conversational Assistants using Model Interpretation based Training Sample Selection (2022.emnlp-industry)
Copied to clipboard
Stefan Schroedl, Manoj Kumar, Kiana Hajebi, Morteza Ziyadi, Sriram Venkatapathy, Anil Ramakrishna, Rahul Gupta, Pradeep Natarajan
| Challenge: | Large-scale, voice-based conversational assistants process each utterance through a multi-stage pipeline that includes wakeword detection, automatic speech recognition (ASR), natural language understanding (NLU), entity resolution, and textto-speech. |
| Approach: | They propose a method to identify customer implicitly satisfied with Alexa's responses by leveraging interpretations of model behavior. |
| Outcome: | The proposed approach produces statistically significant improvements in both offline and online tests. |
Chasing the Tail with Domain Generalization: A Case Study on Frequency-Enriched Datasets (2022.aacl-main)
Copied to clipboard
| Challenge: | In academic research, natural language understanding tasks are typically defined by creating annotated datasets in which each utterance is encountered once. |
| Approach: | They propose a method that explicitly uses utterance frequency in training data to learn models that are more robust to unknown distributions. |
| Outcome: | The proposed approach shows up to 7.02% relative improvement over baselines on the tail data. |