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20122026
most citedTo go deep or wide in learning?

21 citations · 27 across the 14 of their papers we have counts for

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8 papers · 1 filter

cs.CL2026

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…

cs.CL2026

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…

cs.CL2026

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.…

cs.CL2024

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…

cs.CL2022

Mix-and-Match: Scalable Dialog Response Retrieval using Gaussian Mixture Embeddings

Gaurav Pandey, Danish Contractor, Sachindra Joshi

Embedding-based approaches for dialog response retrieval embed the context-response pairs as points in the embedding space. These approaches are scalable, but fail to account for t…

cs.CL2022★ 1 cited

Gaining Insights into Unrecognized User Utterances in Task-Oriented Dialog Systems

Ella Rabinovich, Matan Vetzler, David Boaz +3

The rapidly growing market demand for automatic dialogue agents capable of goal-oriented behavior has caused many tech-industry leaders to invest considerable efforts into task-ori…

cs.CL2021★ 1 cited

Variational Learning for Unsupervised Knowledge Grounded Dialogs

Mayank Mishra, Dhiraj Madan, Gaurav Pandey +1

Recent methods for knowledge grounded dialogs generate responses by incorporating information from an external textual document. These methods do not require the exact document to…

cs.CL2020

Simulated Chats for Building Dialog Systems: Learning to Generate Conversations from Instructions

Biswesh Mohapatra, Gaurav Pandey, Danish Contractor +1

Popular dialog datasets such as MultiWOZ are created by providing crowd workers an instruction, expressed in natural language, that describes the task to be accomplished. Crowd wor…