67 citations · 70 across the 6 of their papers we have counts for
8 papers
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…
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…
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…
Selective Perception: Optimizing State Descriptions with Reinforcement Learning for Language Model Actors
Kolby Nottingham, Yasaman Razeghi, Kyungmin Kim +4
Large language models (LLMs) are being applied as actors for sequential decision making tasks in domains such as robotics and games, utilizing their general world knowledge and pla…
A Theoretically Grounded Benchmark for Evaluating Machine Commonsense
Henrique Santos, Ke Shen, Alice M. Mulvehill +3
Programming machines with commonsense reasoning (CSR) abilities is a longstanding challenge in the Artificial Intelligence community. Current CSR benchmarks use multiple-choice (an…