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Found 5 papers, 1 papers with code
Testing and Understanding Erroneous Planning in LLM Agents through Synthesized User Inputs
These synthesized inputs are natural language paragraphs that specify the requirements for completing a series of tasks.
InstructTA: Instruction-Tuned Targeted Attack for Large Vision-Language Models
Initially, we utilize a public text-to-image generative model to "reverse" the target response into a target image, and employ GPT-4 to infer a reasonable instruction $\boldsymbol{p}^\prime$ from the target response.
Benchmarking and Explaining Large Language Model-based Code Generation: A Causality-Centric Approach
We illustrate the insights that our framework can provide by studying over 3 popular LLMs with over 12 prompt adjustment strategies.
Enabling Runtime Verification of Causal Discovery Algorithms with Automated Conditional Independence Reasoning (Extended Version)
Based on the decision procedure to CIR, CICheck includes two variants: ED-CICheck and ED-CICheck, which detect erroneous CI tests (to enhance reliability) and prune excessive CI tests (to enhance privacy), respectively.
Causality-Aided Trade-off Analysis for Machine Learning Fairness
This paper uses causality analysis as a principled method for analyzing trade-offs between fairness parameters and other crucial metrics in ML pipelines.
