16 citations · 35 across the 23 of their papers we have counts for
24 papers
RIT-RAG: Navigating Document Corpora with Retrieval-Induced Trees
Meghanadh Pulivarthi, Swaraj Kumar Biswal, Kushagra Bhushan +3
Retrieval-augmented generation (RAG) grounds language models in external corpora. Agentic RAG enables iterative search, yet exposes the model to isolated chunks without document st…
DKL: Decoupled Knowledge Learning for Instruction-Tuned Language Models
Kushagra Bhushan, Meghanadh Pulivarthi, Sai Krishna Reddy Sathi +7
RAG has become the de facto method for incorporating new, corpus-specific knowledge into an instruction following LLM (Instruct LLM). Although RAG-based prompting improves factual…
KItCAT: Knowledge Injection via Input Corruption for Auto-regressive Training
Meghanadh Pulivarthi, Kushagra Bhushan, Vineet Kumar +5
LLMs acquire vast amounts of knowledge during pre-training, but often lack the specialized knowledge needed to answer questions from niche sources such as manuals or technical docu…
ToolWeave: Structured Synthesis of Complex Multi-Turn Tool-Calling Dialogues
Dinesh Khandelwal, Gnana Prakash Punnavajhala, GPS Bhargav +4
Multi-turn tool calling is essential for LLMs to function as autonomous agents, yet synthesizing the training data required for these capabilities remains a fundamental challenge.…
A Library of LLM Intrinsics for Retrieval-Augmented Generation
Marina Danilevsky, Kristjan Greenewald, Chulaka Gunasekara +13
In the developer community for large language models (LLMs), there is not yet a clean pattern analogous to a software library, to support very large scale collaboration. Even for t…
Systematic Knowledge Injection into Large Language Models via Diverse Augmentation for Domain-Specific RAG
Kushagra Bhushan, Yatin Nandwani, Dinesh Khandelwal +4
Retrieval-Augmented Generation (RAG) has emerged as a prominent method for incorporating domain knowledge into Large Language Models (LLMs). While RAG enhances response relevance b…
Selective Self-to-Supervised Fine-Tuning for Generalization in Large Language Models
Sonam Gupta, Yatin Nandwani, Asaf Yehudai +3
Fine-tuning Large Language Models (LLMs) on specific datasets is a common practice to improve performance on target tasks. However, this performance gain often leads to overfitting…
MediTOD: An English Dialogue Dataset for Medical History Taking with Comprehensive Annotations
Vishal Vivek Saley, Goonjan Saha, Rocktim Jyoti Das +2
Medical task-oriented dialogue systems can assist doctors by collecting patient medical history, aiding in diagnosis, or guiding treatment selection, thereby reducing doctor burnou…
Selective Self-Rehearsal: A Fine-Tuning Approach to Improve Generalization in Large Language Models
Sonam Gupta, Yatin Nandwani, Asaf Yehudai +4
Fine-tuning Large Language Models (LLMs) on specific datasets is a common practice to improve performance on target tasks. However, this performance gain often leads to overfitting…
Granite-Function Calling Model: Introducing Function Calling Abilities via Multi-task Learning of Granular Tasks
Ibrahim Abdelaziz, Kinjal Basu, Mayank Agarwal +23
Large language models (LLMs) have recently shown tremendous promise in serving as the backbone to agentic systems, as demonstrated by their performance in multi-faceted, challengin…
Synergizing In-context Learning with Hints for End-to-end Task-oriented Dialog Systems
Vishal Vivek Saley, Rocktim Jyoti Das, Dinesh Raghu +1
End-to-end Task-Oriented Dialog (TOD) systems typically require extensive training datasets to perform well. In contrast, large language model (LLM) based TOD systems can excel eve…
Few shot chain-of-thought driven reasoning to prompt LLMs for open ended medical question answering
Saeel Sandeep Nachane, Ojas Gramopadhye, Prateek Chanda +5
In this paper, we propose a modified version of the MedQA-USMLE dataset, named MEDQA-OPEN, which contains open-ended medical questions without options to mimic clinical scenarios,…
BRAIn: Bayesian Reward-conditioned Amortized Inference for natural language generation from feedback
Gaurav Pandey, Yatin Nandwani, Tahira Naseem +6
Distribution matching methods for language model alignment such as Generation with Distributional Control (GDC) and Distributional Policy Gradient (DPG) have not received the same…
DKAF: KB Arbitration for Learning Task-Oriented Dialog Systems with Dialog-KB Inconsistencies
Vishal Vivek Saley, Rocktim Jyoti Das, Dinesh Raghu +1
Task-oriented dialog (TOD) agents often ground their responses on external knowledge bases (KBs). These KBs can be dynamic and may be updated frequently. Existing approaches for le…
Pointwise Mutual Information Based Metric and Decoding Strategy for Faithful Generation in Document Grounded Dialogs
Yatin Nandwani, Vineet Kumar, Dinesh Raghu +2
A major concern in using deep learning based generative models for document-grounded dialogs is the potential generation of responses that are not \textit{faithful} to the underlyi…
Joint Reasoning on Hybrid-knowledge sources for Task-Oriented Dialog
Mayank Mishra, Danish Contractor, Dinesh Raghu
Traditional systems designed for task oriented dialog utilize knowledge present only in structured knowledge sources to generate responses. However, relevant information required t…
Matching Papers and Reviewers at Large Conferences
Kevin Leyton-Brown, Mausam, Yatin Nandwani +4
Peer-reviewed conferences, the main publication venues in CS, rely critically on matching highly qualified reviewers for each paper. Because of the growing scale of these conferenc…
Constraint based Knowledge Base Distillation in End-to-End Task Oriented Dialogs
Dinesh Raghu, Atishya Jain, Mausam +1
End-to-End task-oriented dialogue systems generate responses based on dialog history and an accompanying knowledge base (KB). Inferring those KB entities that are most relevant for…
End-to-End Learning of Flowchart Grounded Task-Oriented Dialogs
Dinesh Raghu, Shantanu Agarwal, Sachindra Joshi +1
We propose a novel problem within end-to-end learning of task-oriented dialogs (TOD), in which the dialog system mimics a troubleshooting agent who helps a user by diagnosing their…
Unsupervised Learning of KB Queries in Task-Oriented Dialogs
Dinesh Raghu, Nikhil Gupta, Mausam
Task-oriented dialog (TOD) systems often need to formulate knowledge base (KB) queries corresponding to the user intent and use the query results to generate system responses. Exis…
Mask & Focus: Conversation Modelling by Learning Concepts
Gaurav Pandey, Dinesh Raghu, Sachindra Joshi
Sequence to sequence models attempt to capture the correlation between all the words in the input and output sequences. While this is quite useful for machine translation where the…
Unsupervised Learning of Interpretable Dialog Models
Dhiraj Madan, Dinesh Raghu, Gaurav Pandey +1
Recently several deep learning based models have been proposed for end-to-end learning of dialogs. While these models can be trained from data without the need for any additional a…
Multi-level Memory for Task Oriented Dialogs
Revanth Reddy, Danish Contractor, Dinesh Raghu +1
Recent end-to-end task oriented dialog systems use memory architectures to incorporate external knowledge in their dialogs. Current work makes simplifying assumptions about the str…
Disentangling Language and Knowledge in Task-Oriented Dialogs
Dinesh Raghu, Nikhil Gupta, Mausam
The Knowledge Base (KB) used for real-world applications, such as booking a movie or restaurant reservation, keeps changing over time. End-to-end neural networks trained for these…