Search Results for author:
Found 7 papers, 5 papers with code
Procedural Knowledge in Pretraining Drives Reasoning in Large Language Models
We find that, while the models rely on mostly distinct sets of data for each factual question, a document often has a similar influence across different reasoning questions within the same task, indicating the presence of procedural knowledge.
Towards Reliable Evaluation of Behavior Steering Interventions in LLMs
Representation engineering methods have recently shown promise for enabling efficient steering of model behavior.
Debating with More Persuasive LLMs Leads to More Truthful Answers
In anticipation of this, we ask: can weaker models assess the correctness of stronger models?
The Goldilocks of Pragmatic Understanding: Fine-Tuning Strategy Matters for Implicature Resolution by LLMs
We present our findings as the starting point for further research into evaluating how LLMs interpret language in context and to drive the development of more pragmatic and useful models of human discourse.
Improving Systematic Generalization Through Modularity and Augmentation
After training on an augmented dataset with almost forty times more adverbs than the original problem, a non-modular baseline is not able to systematically generalize to a novel combination of a known verb and adverb.
A Benchmark for Systematic Generalization in Grounded Language Understanding
In this paper, we introduce a new benchmark, gSCAN, for evaluating compositional generalization in situated language understanding.
Insertion-Deletion Transformer
We propose the Insertion-Deletion Transformer, a novel transformer-based neural architecture and training method for sequence generation.
