1 citations · 3 across the 6 of their papers we have counts for
6 papers
Think Before You Lie: How Reasoning Leads to Honesty
Ann Yuan, Asma Ghandeharioun, Carter Blum +6
While existing evaluations of large language models (LLMs) measure deception rates, the underlying conditions that give rise to deceptive behavior are poorly understood. We investi…
Beyond the Rosetta Stone: Unification Forces in Generalization Dynamics
Carter Blum, Katja Filippova, Ann Yuan +8
Large language models (LLMs) struggle with cross-lingual knowledge transfer: they sometimes hallucinate when asked in one language about facts expressed in a different language dur…
Improving Neutral Point-of-View Generation with Data- and Parameter-Efficient RL
Jessica Hoffmann, Christiane Ahlheim, Zac Yu +8
The paper shows that parameter-efficient reinforcement learning (PE-RL) is a highly effective training regime to improve large language models' (LLMs) ability to answer queries on…
Detecting Hallucination and Coverage Errors in Retrieval Augmented Generation for Controversial Topics
Tyler A. Chang, Katrin Tomanek, Jessica Hoffmann +4
We explore a strategy to handle controversial topics in LLM-based chatbots based on Wikipedia's Neutral Point of View (NPOV) principle: acknowledge the absence of a single true ans…
Parameter Efficient Reinforcement Learning from Human Feedback
Hakim Sidahmed, Samrat Phatale, Alex Hutcheson +16
While Reinforcement Learning from Human Feedback (RLHF) effectively aligns pretrained Large Language and Vision-Language Models (LLMs, and VLMs) with human preferences, its computa…
Decoding-time Realignment of Language Models
Tianlin Liu, Shangmin Guo, Leonardo Bianco +7
Aligning language models with human preferences is crucial for reducing errors and biases in these models. Alignment techniques, such as reinforcement learning from human feedback…