2 citations · 9 across the 33 of their papers we have counts for
7 papers · 2 filters
DuplexSpeechBench-Document Grounding: Benchmarking Document Grounding and Hallucinations in Voice Agents
Puneet Mathur, Nedim Lipka, Zeyu Jin +1
Voice agents enable low-latency, natural interaction, yet their ability to faithfully ground responses in external documents remains underexplored. We introduce DuplexSpeechBench-D…
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…
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…
Blind to the Human Touch: Overlap Bias in LLM-Based Summary Evaluation
Jiangnan Fang, Cheng-Tse Liu, Hanieh Deilamsalehy +5
Large language model (LLM) judges have often been used alongside traditional, algorithm-based metrics for tasks like summarization because they better capture semantic information,…
Reasoning-Based Personalized Generation for Users with Sparse Data
Bo Ni, Branislav Kveton, Samyadeep Basu +14
Large Language Model (LLM) personalization holds great promise for tailoring responses by leveraging personal context and history. However, real-world users usually possess sparse…