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cs.LG2024★ 2 cited
LICO: Large Language Models for In-Context Molecular Optimization
Tung Nguyen, Aditya Grover
Optimizing black-box functions is a fundamental problem in science and engineering. To solve this problem, many approaches learn a surrogate function that estimates the underlying…
cs.LG2024
Probing the Decision Boundaries of In-context Learning in Large Language Models
Siyan Zhao, Tung Nguyen, Aditya Grover
In-context learning is a key paradigm in large language models (LLMs) that enables them to generalize to new tasks and domains by simply prompting these models with a few exemplars…
cs.CL2024
Comparing Bad Apples to Good Oranges: Aligning Large Language Models via Joint Preference Optimization
Hritik Bansal, Ashima Suvarna, Gantavya Bhatt +3
A common technique for aligning large language models (LLMs) relies on acquiring human preferences by comparing multiple generations conditioned on a fixed context. This method, ho…