Summary of Cbeval: a Framework For Evaluating and Interpreting Cognitive Biases in Llms, by Ammar Shaikh et al.
CBEval: A framework for evaluating and interpreting cognitive biases in LLMs
by Ammar Shaikh, Raj Abhijit Dandekar, Sreedath Panat, Rajat Dandekar
First submitted to arxiv on: 4 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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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 research paper presents a framework to interpret and understand the cognitive biases present in Large Language Models (LLMs). Despite their improved performance on benchmarks, LLMs exhibit gaps in their reasoning capabilities. The study reveals limitations and biases in these models, such as round number bias and framing effect, by constructing influence graphs that identify phrases and words responsible for biases. This framework provides insights into the decision-making processes of LLMs and has implications for their potential applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how Large Language Models think and make decisions. It shows that even though these models are very good at certain tasks, they still have some limitations in how they reason. The research also finds that these models can inherit biases from the data they were trained on, which could be a problem if we want to use them for important tasks like making decisions or summarizing information. The study uses special graphs to identify what parts of language are most responsible for these biases. |
