21 citations · 28 across the 12 of their papers we have counts for
7 papers · 1 filter
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…
Meta-Reasoning Improves Tool Use in Large Language Models
Lisa Alazraki, Marek Rei
External tools help large language models succeed at tasks where they would otherwise typically fail. In existing frameworks, choosing tools at test time relies on naive greedy dec…
A Multilingual Virtual Guide for Self-Attachment Technique
Alicia Jiayun Law, Ruoyu Hu, Lisa Alazraki +3
In this work, we propose a computational framework that leverages existing out-of-language data to create a conversational agent for the delivery of Self-Attachment Technique (SAT)…