7 citations · 13 across the 10 of their papers we have counts for
5 papers · 2 filters
LLM economicus? Mapping the Behavioral Biases of LLMs via Utility Theory
Jillian Ross, Yoon Kim, Andrew W. Lo
Humans are not homo economicus (i.e., rational economic beings). As humans, we exhibit systematic behavioral biases such as loss aversion, anchoring, framing, etc., which lead us t…
Lookback Lens: Detecting and Mitigating Contextual Hallucinations in Large Language Models Using Only Attention Maps
Yung-Sung Chuang, Linlu Qiu, Cheng-Yu Hsieh +3
When asked to summarize articles or answer questions given a passage, large language models (LLMs) can hallucinate details and respond with unsubstantiated answers that are inaccur…
MDCR: A Dataset for Multi-Document Conditional Reasoning
Peter Baile Chen, Yi Zhang, Chunwei Liu +3
The same real-life questions posed to different individuals may lead to different answers based on their unique situations. For instance, whether a student is eligible for a schola…
Paraphrase and Solve: Exploring and Exploiting the Impact of Surface Form on Mathematical Reasoning in Large Language Models
Yue Zhou, Yada Zhu, Diego Antognini +2
This paper studies the relationship between the surface form of a mathematical problem and its solvability by large language models. We find that subtle alterations in the surface…
CHAMP: A Competition-level Dataset for Fine-Grained Analyses of LLMs' Mathematical Reasoning Capabilities
Yujun Mao, Yoon Kim, Yilun Zhou
Recent large language models (LLMs) have shown indications of mathematical reasoning ability on challenging competition-level problems, especially with self-generated verbalization…