Abstract
Findings Assoc. Comput. Linguistics: EMNLP 2024 15447-15459 (2024) Large language model (LLM) agents show promise in an increasing number of
domains. In many proposed applications, it is expected that the agent reasons
over accumulated experience presented in an input prompt. We propose the OEDD
(Operationalize Experience Despite Distraction) corpus, a
human-annotator-validated body of scenarios with pre-scripted agent histories
where the agent must make a decision based on disparate experiential
information in the presence of a distractor. We evaluate three state-of-the-art
LLMs (GPT-3.5 Turbo, GPT-4o, and Gemini 1.5 Pro) using a minimal
chain-of-thought prompting strategy and observe that when (1) the input context
contains over 1,615 tokens of historical interactions, (2) a crucially
decision-informing premise is the rightful conclusion over two disparate
environment premises, and (3) a trivial, but distracting red herring fact
follows, all LLMs perform worse than random choice at selecting the better of
two actions. Our code and test corpus are publicly available at:
https://github.com/sonnygeorge/OEDD .