activity
20172026
most citedNeural Assistant: Joint Action Prediction, Response Generation, and Latent Knowledge Reasoning

13 citations · 31 across the 16 of their papers we have counts for

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20 papers · 1 filter

cs.CL20261 cited

InfMem: Learning System-2 Memory Control for Long-Context Agent

Xinyu Wang, Mingze Li, Peng Lu +6

Reasoning over ultra-long documents requires synthesizing sparse evidence scattered across distant segments under strict memory constraints. While streaming agents enable scalable…

cs.CL2025

Resona: Improving Context Copying in Linear Recurrence Models with Retrieval

Xinyu Wang, Linrui Ma, Jerry Huang +5

Recent shifts in the space of large language model (LLM) research have shown an increasing focus on novel architectures to compete with prototypical Transformer-based models that h…

cs.CL2024

Do Robot Snakes Dream like Electric Sheep? Investigating the Effects of Architectural Inductive Biases on Hallucination

Jerry Huang, Prasanna Parthasarathi, Mehdi Rezagholizadeh +2

The growth in prominence of large language models (LLMs) in everyday life can be largely attributed to their generative abilities, yet some of this is also owed to the risks and co…

cs.CL2024

EWEK-QA: Enhanced Web and Efficient Knowledge Graph Retrieval for Citation-based Question Answering Systems

Mohammad Dehghan, Mohammad Ali Alomrani, Sunyam Bagga +12

The emerging citation-based QA systems are gaining more attention especially in generative AI search applications. The importance of extracted knowledge provided to these systems i…

cs.CL2024

CHARP: Conversation History AwaReness Probing for Knowledge-grounded Dialogue Systems

Abbas Ghaddar, David Alfonso-Hermelo, Philippe Langlais +3

In this work, we dive deep into one of the popular knowledge-grounded dialogue benchmarks that focus on faithfulness, FaithDial. We show that a significant portion of the FaithDial…

cs.CL20242 cited

Towards Practical Tool Usage for Continually Learning LLMs

Jerry Huang, Prasanna Parthasarathi, Mehdi Rezagholizadeh +1

Large language models (LLMs) show an innate skill for solving language based tasks. But insights have suggested an inability to adjust for information or task-solving skills becomi…