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20242026
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cs.CL2026

MultAttnAttrib: Training-Free Multimodal Attribution in Long Document Question Answering

Dang Quang Thien Tran, Quang V. Dang, Vinamra Tyagi +7

As grounded QA systems are increasingly deployed in AI assistants, accurately attributing generated answers to evidence is critical for user trust and model safety. While unimodal…

cs.CL2026

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…

cs.CL2026

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…

cs.CL2025

Decomposition-Enhanced Training for Post-Hoc Attributions In Language Models

Sriram Balasubramanian, Samyadeep Basu, Koustava Goswami +6

Large language models (LLMs) are increasingly used for long-document question answering, where reliable attribution to sources is critical for trust. Existing post-hoc attribution…

cs.CL2025

A Closer Look at Bias and Chain-of-Thought Faithfulness of Large (Vision) Language Models

Sriram Balasubramanian, Samyadeep Basu, Soheil Feizi

Chain-of-thought (CoT) reasoning enhances performance of large language models, but questions remain about whether these reasoning traces faithfully reflect the internal processes…

cs.CL2025

Hop, Skip, and Overthink: Diagnosing Why Reasoning Models Fumble during Multi-Hop Analysis

Anushka Yadav, Isha Nalawade, Srujana Pillarichety +7

The emergence of reasoning models and their integration into practical AI chat bots has led to breakthroughs in solving advanced math, deep search, and extractive question answerin…