6 papers · 1 filter
LLMs Can't Handle Peer Pressure: Crumbling under Multi-Agent Social Interactions
Maojia Song, Tej Deep Pala, Ruiwen Zhou +5
Large language models (LLMs) are increasingly integrated into multi-agent systems (MAS), where peer interactions shape individual decisions. While prior work has mainly examined co…
Lessons from Training Grounded LLMs with Verifiable Rewards
Shang Hong Sim, Tej Deep Pala, Vernon Toh +5
Generating grounded and trustworthy responses remains a key challenge for large language models (LLMs). While retrieval-augmented generation (RAG) with citation-based grounding hol…
Error Typing for Smarter Rewards: Improving Process Reward Models with Error-Aware Hierarchical Supervision
Tej Deep Pala, Panshul Sharma, Amir Zadeh +2
Large Language Models (LLMs) are prone to hallucination, especially during multi-hop and reasoning-intensive tasks such as mathematical problem solving. While Outcome Reward Models…
VeriFastScore: Speeding up long-form factuality evaluation
Rishanth Rajendhran, Amir Zadeh, Matthew Sarte +2
Metrics like FactScore and VeriScore that evaluate long-form factuality operate by decomposing an input response into atomic claims and then individually verifying each claim. Whil…
BLEUBERI: BLEU is a surprisingly effective reward for instruction following
Yapei Chang, Yekyung Kim, Michael Krumdick +4
Reward models are central to aligning LLMs with human preferences, but they are costly to train, requiring large-scale human-labeled preference data and powerful pretrained LLM bac…
PromptDistill: Query-based Selective Token Retention in Intermediate Layers for Efficient Large Language Model Inference
Weisheng Jin, Maojia Song, Tej Deep Pala +4
As large language models (LLMs) tackle increasingly complex tasks and longer documents, their computational and memory costs during inference become a major bottleneck. To address…