Papers by Mohammad Kachuee
GrounDial: Human-norm Grounded Safe Dialog Response Generation (2024.findings-eacl)
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| Challenge: | Recent conversational AI systems generate unsafe responses agreeing to offensive user input or including toxic content. |
| Approach: | They propose a method where response safety is achieved by grounding responses to commonsense social rules without fine-tuning. |
| Outcome: | The proposed approach is quantitatively and qualitatively safer even without additional data or tuning. |
Scalable and Robust Self-Learning for Skill Routing in Large-Scale Conversational AI Systems (2022.naacl-industry)
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| Challenge: | Existing methods to enable skill routing do not scale in terms of the number of skills and skill on-boarding. |
| Approach: | They propose a model-based approach to enable natural conversation by allowing frequent policy updates . they propose an annotation-based system, rule-based model, and bandit-based learning . |
| Outcome: | The proposed method is scalable and cost-effective, the authors show . they show that it can improve the user experience without abrupt policy changes . |
Skin-in-the-Game: Decision Making via Multi-Stakeholder Alignment in LLMs (2024.acl-long)
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| Challenge: | Large Language Models (LLMs) have shown remarkable capabilities in tasks such as summarization, arithmetic reasoning, and question answering. |
| Approach: | They propose a framework that explores decisions’ consequences from multiple stakeholder perspectives and a SKIG framework to enhance moral reasoning in large language models. |
| Outcome: | The proposed framework exhibits marked improvements compared to baselines across different language models and benchmarks. |
Improving Tool Retrieval by Leveraging Large Language Models for Query Generation (2025.coling-industry)
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| Challenge: | Large Language Models (LLMs) have shown great promise in common sense language understanding, conversational fluency, and reasoning. |
| Approach: | They propose to use Large Language Models to generate a retrieval query and embed it into the prompt to find relevant tools via a nearest-neighbor search. |
| Outcome: | The proposed method improves retrieval for in-domain (seen tools) and out-of-domain settings. |
Knowledge Extraction on Semi-Structured Content: Does It Remain Relevant for Question Answering in the Era of LLMs? (2026.eacl-long)
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Kai Sun, Yin Huang, Srishti Mehra, Mohammad Kachuee, Xilun Chen, Renjie Tao, Zhaojiang Lin, Andrea Jessee, Nirav Shah, Alex L Betty, Yue Liu, Anuj Kumar, Wen-tau Yih, Xin Luna Dong
| Challenge: | Existing literature on knowledge extraction for question answering questions whether it is still relevant for question answerrs. |
| Approach: | They extend an existing benchmark with knowledge extraction annotations and evaluate commercial and open-source LLMs of varying sizes. |
| Outcome: | The proposed model can achieve high QA accuracy, but can still benefit from knowledge extraction through augmentation with extracted triples and multi-task learning. |
Self-Supervised Contrastive Learning for Efficient User Satisfaction Prediction in Conversational Agents (2021.naacl-main)
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| Challenge: | End-to-end deep learning methods that focus on user satisfaction are challenging due to the required annotation costs and turnaround times. |
| Approach: | They propose a self-supervised contrastive learning approach that leverages the pool of unlabeled data to learn user-agent interactions. |
| Outcome: | The proposed approach reduces the required number of annotations while improving generalization on unseen skills. |
PrismRAG: Boosting RAG Factuality with Distractor Resilience and Strategized Reasoning (2025.emnlp-industry)
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Mohammad Kachuee, Teja Gollapudi, Minseok Kim, Yin Huang, Kai Sun, Xiao Yang, Jiaqi Wang, Nirav Shah, Yue Liu, Aaron Colak, Anuj Kumar, Wen-tau Yih, Xin Luna Dong
| Challenge: | Existing methods to improve factuality of large language models (LLMs) rely on human-engineered instructions. |
| Approach: | They propose a retrieval-augmented generation framework that trains the model with distractor-aware QA pairs mixing gold evidence with subtle distractor passages and instills reasoning-centric habits that make the LLM plan, rationalize, and synthesize without extensive human engineered instructions. |
| Outcome: | The proposed framework outperforms state-of-the-art solutions across 12 open-book RAG QA benchmarks and is being deployed in production. |
Planning and Editing What You Retrieve for Enhanced Tool Learning (2024.findings-naacl)
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| Challenge: | Existing methods for integrating external tools with Large Language Models fall short on effectively shortlisting relevant tools. |
| Approach: | They propose a plan-and-retrieve and edit-and ground paradigms for LLMs that decompose complex queries into actionable tasks. |
| Outcome: | The proposed paradigms significantly improve recall and NDCG in tool retrieval tasks, surpassing current state-of-the-art models. |
Scalable and Safe Remediation of Defective Actions in Self-Learning Conversational Systems (2023.acl-industry)
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| Challenge: | Off-Policy reinforcement learning has been used to improve conversational AIs, but in large-scale commercial environments it is challenging to balance between policy improvements and experience continuity. |
| Approach: | They propose to curate and leverage regression incident reports to validate, safe-guard, and improve policies prior to the online deployment. |
| Outcome: | The proposed method validates, safe-guards, and improves policies prior to the online deployment. |
Constrained Policy Optimization for Controlled Self-Learning in Conversational AI Systems (2023.acl-industry)
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| Challenge: | Recent self-learning methods based on user satisfaction metrics and contextual bandits have shown promising results to enable consistent improvements in conversational AI systems. |
| Approach: | They propose a meta-gradient learning approach that adjusts constraint violation penalty terms adaptively through a user-defined meta objective that encourages balanced constraint satisfaction across domains. |
| Outcome: | The proposed framework supports fine-grained exploration targets for individual domains via user-defined constraints. |