34 citations · 134 across the 57 of their papers we have counts for
7 papers · 1 filter
Lightweight Latent Reasoning for Narrative Tasks
Alexander Gurung, Esmeralda S. Whitammer, Mirella Lapata
Large language models (LLMs) tackle complex tasks by generating long chains of thought or "reasoning traces" that act as latent variables in the generation of an output given a que…
Mixtures of In-Context Learners
Giwon Hong, Emile van Krieken, Edoardo Ponti +2
In-context learning (ICL) adapts LLMs by providing demonstrations without fine-tuning the model parameters; however, it does not differentiate between demonstrations and quadratica…
Proof Flow: Preliminary Study on Generative Flow Network Language Model Tuning for Formal Reasoning
Matthew Ho, Vincent Zhu, Xiaoyin Chen +3
Reasoning is a fundamental substrate for solving novel and complex problems. Deliberate efforts in learning and developing frameworks around System 2 reasoning have made great stri…
Learning diverse attacks on large language models for robust red-teaming and safety tuning
Seanie Lee, Minsu Kim, Lynn Cherif +8
Red-teaming, or identifying prompts that elicit harmful responses, is a critical step in ensuring the safe and responsible deployment of large language models (LLMs). Developing ef…
ThinkSum: Probabilistic reasoning over sets using large language models
Batu Ozturkler, Nikolay Malkin, Zhen Wang +1
Large language models (LLMs) have a substantial capacity for high-level analogical reasoning: reproducing patterns in linear text that occur in their training data (zero-shot evalu…
Coherence boosting: When your pretrained language model is not paying enough attention
Nikolay Malkin, Zhen Wang, Nebojsa Jojic
Long-range semantic coherence remains a challenge in automatic language generation and understanding. We demonstrate that large language models have insufficiently learned the effe…