Summary of Probing the Capacity Of Language Model Agents to Operationalize Disparate Experiential Context Despite Distraction, by Sonny George et al.
Probing the Capacity of Language Model Agents to Operationalize Disparate Experiential Context Despite Distraction
by Sonny George, Chris Sypherd, Dylan Cashman
First submitted to arxiv on: 19 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates the capabilities of large language model (LLM) agents in decision-making scenarios. The authors propose the OEDD corpus, a dataset featuring human-annotated scenarios where an LLM must make decisions based on prior experiences presented as input prompts, despite distractors. The study evaluates 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. Surprisingly, the models perform worse than random choice when confronted with complex decision-making scenarios involving over 1,615 tokens of historical interactions, multiple environment premises, and distracting red herrings. The authors’ findings highlight the limitations of current LLMs in operationalizing experience despite distraction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how well big language models can make decisions when given information from the past. Researchers created a special dataset with scenarios that test these models’ abilities to think critically and ignore distractions. They tested three top-performing language models on this task and found that they struggled to make good decisions when faced with complex situations involving lots of historical data, multiple factors to consider, and distracting details. The results show that current language models have limitations in using past experiences to make informed decisions. |
Keywords
» Artificial intelligence » Gemini » Gpt » Large language model » Prompting
