Papers by Saurav Sahay
Put Chatbot into Its Interlocutor’s Shoes: New Framework to Learn Chatbot Responding with Intention (2021.naacl-main)
Copied to clipboard
| Challenge: | Currently, most work on improving the fluency and coherence of chatbots is focused on making them more human-like. |
| Approach: | They propose a framework to train chatbots to possess human-like intentions by making them learn from interactive conversation. |
| Outcome: | The proposed framework includes a guiding chatbot and an interlocutor model that plays the role of humans. |
Refine and Imitate: Reducing Repetition and Inconsistency in Persuasion Dialogues via Reinforcement Learning and Human Demonstration (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Persuasion dialogue systems have long-standing problems of dialogue repetition and inconsistency which could impact user experience and impede the persuaded outcome. |
| Approach: | They propose to refine a language model baseline without user simulators and distill sentence-level information about repetition, inconsistency, and task relevance through rewards. |
| Outcome: | The proposed model outperforms state-of-the-art models on automatic metrics and human evaluation results on a donation persuasion task and generates more diverse, consistent and persuasive conversations according to user feedback. |
Systematic Analysis for Pretrained Language Model Priming for Parameter-Efficient Fine-tuning (2024.naacl-srw)
Copied to clipboard
| Challenge: | Parameter-efficient (PE) methods for adapting pre-trained language models to downstream tasks are still lacking in many cases. |
| Approach: | They propose a general PE priming framework to enhance few-shot adaptation and generalization ability of PE methods. |
| Outcome: | The proposed framework reveals that the best priming strategy facilitates adaptation to target tasks. |
Safeguard Fine-Tuned LLMs Through Pre- and Post-Tuning Model Merging (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Fine-tuning large language models for downstream tasks often leads to catastrophic forgetting, notably degrading the safety of original alignments. |
| Approach: | They propose to merge the weights of pre- and post-fine-tuned models to improve safety while enhancing performance. |
| Outcome: | Experiments across different downstream tasks and models validate the method’s practicality and effectiveness. |
Seamlessly Integrating Factual Information and Social Content with Persuasive Dialogue (2022.aacl-main)
Copied to clipboard
| Challenge: | Persuasive dialogue systems are designed for chatbots to communicate with and influence users with specific goals. |
| Approach: | They propose a modular dialogue system framework that integrates factual information and social content into persuasive dialogues. |
| Outcome: | The proposed framework is generalizable to any dialogue tasks that have mixed social and task contents. |
Data Augmentation with Paraphrase Generation and Entity Extraction for Multimodal Dialogue System (2022.lrec-1)
Copied to clipboard
| Challenge: | Contextually aware intelligent agents are often required to understand the users and their surroundings in real-time. |
| Approach: | They propose to build a multimodal dialogue system for children learning basic math concepts using limited datasets. |
| Outcome: | The proposed system improves the Natural Language Understanding (NLU) module of a task-oriented SDS pipeline with limited dataset resources. |
Cue-bot: A Conversational Agent for Assistive Technology (2022.acl-demo)
Copied to clipboard
Shachi H Kumar, Hsuan Su, Ramesh Manuvinakurike, Maximilian C. Pinaroc, Sai Prasad, Saurav Sahay, Lama Nachman
| Challenge: | Large-scale pre-training has achieved significant performance gains across many tasks within NLP, including intent prediction and dialogue state tracking. |
| Approach: | They propose to use eye-tracking, mouse controls and an intelligent agent Cue-bot to represent the user in a conversation. |
| Outcome: | The proposed system can be used by people with different levels of disabilities to interact with the world, supported by eye-tracking, mouse controls and an intelligent agent Cue-bot. |