1 citations · 2 across the 6 of their papers we have counts for
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AgentCoMa: A Compositional Benchmark Mixing Commonsense and Mathematical Reasoning in Real-World Scenarios
Lisa Alazraki, Lihu Chen, Ana Brassard +3
Large Language Models (LLMs) have achieved high accuracy on complex commonsense and mathematical problems that involve the composition of multiple reasoning steps. However, current…
Improving the OOD Performance of Closed-Source LLMs on NLI Through Strategic Data Selection
Joe Stacey, Lisa Alazraki, Aran Ubhi +3
We investigate the robustness of fine-tuned Large Language Models (LLMs) for the task of Natural Language Inference (NLI), finding that the in-distribution gains from fine-tuning c…
Reverse Engineering Human Preferences with Reinforcement Learning
Lisa Alazraki, Tan Yi-Chern, Jon Ander Campos +3
The capabilities of Large Language Models (LLMs) are routinely evaluated by other LLMs trained to predict human preferences. This framework--known as LLM-as-a-judge--is highly scal…
Enhancing LLM Robustness to Perturbed Instructions: An Empirical Study
Aryan Agrawal, Lisa Alazraki, Shahin Honarvar +1
Large Language Models (LLMs) are highly vulnerable to input perturbations, as even a small prompt change may result in a substantially different output. Existing methods to enhance…
No Need for Explanations: LLMs can implicitly learn from mistakes in-context
Lisa Alazraki, Maximilian Mozes, Jon Ander Campos +3
Showing incorrect answers to Large Language Models (LLMs) is a popular strategy to improve their performance in reasoning-intensive tasks. It is widely assumed that, in order to be…