4 papers
Nudging: Inference-time Alignment of LLMs via Guided Decoding
Yu Fei, Yasaman Razeghi, Sameer Singh
Large language models (LLMs) require alignment to effectively and safely follow user instructions. This process necessitates training an aligned version for every base model, resul…
OptiSeq: Ordering Examples On-The-Fly for In-Context Learning
Rahul Atul Bhope, Praveen Venkateswaran, K. R. Jayaram +3
Developers using LLMs and LLM-based agents in their applications have provided plenty of anecdotal evidence that in-context-learning (ICL) is fragile. In this paper, we show that i…
Are Models Biased on Text without Gender-related Language?
Catarina G Belém, Preethi Seshadri, Yasaman Razeghi +1
Gender bias research has been pivotal in revealing undesirable behaviors in large language models, exposing serious gender stereotypes associated with occupations, and emotions. A…
EchoPrompt: Instructing the Model to Rephrase Queries for Improved In-context Learning
Rajasekhar Reddy Mekala, Yasaman Razeghi, Sameer Singh
Language models are achieving impressive performance on various tasks by aggressively adopting inference-time prompting techniques, such as zero-shot and few-shot prompting. In thi…