18 papers · 1 filter
DIAGRAMS: A Review Framework for Reasoning-Level Attribution in Diagram QA
Anirudh Iyengar Kaniyar Narayana Iyengar, Tampu Ravi Kumar, Manan Suri +4
Diagram question answering (Diagram QA) requires reasoning-level attribution that links each question-answer pair to all visual regions needed to derive the answer, rather than onl…
A Survey on LLM-based Conversational User Simulation
Bo Ni, Leyao Wang, Yu Wang +27
User simulation has long played a vital role in computer science due to its potential to support a wide range of applications. Language, as the primary medium of human communicatio…
Lizard: An Efficient Linearization Framework for Large Language Models
Chien Van Nguyen, Huy Nguyen, Ruiyi Zhang +10
We propose Lizard, a linearization framework that transforms pretrained Transformer-based Large Language Models (LLMs) into subquadratic architectures. Transformers faces severe co…
Structured Uncertainty guided Clarification for LLM Agents
Manan Suri, Puneet Mathur, Nedim Lipka +3
LLM agents with tool-calling capabilities often fail when user instructions are ambiguous or incomplete, leading to incorrect invocations and task failures. Existing approaches ope…
Cluster-R1: Large Reasoning Models Are Instruction-following Clustering Agents
Peijun Qing, Puneet Mathur, Nedim Lipka +5
General-purpose embedding models excel at recognizing semantic similarities but fail to capture the characteristics of texts specified by user instructions. In contrast, instructio…
TraceBack: Multi-Agent Decomposition for Fine-Grained Table Attribution
Tejas Anvekar, Junha Park, Rajat Jha +4
Question answering (QA) over structured tables requires not only accurate answers but also transparency about which cells support them. Existing table QA systems rarely provide fin…