1 citations · 2 across the 4 of their papers we have counts for
5 papers
Addressing Over-Refusal in LLMs with Competing Rewards
Taeyoun Kim, Aviral Kumar
Safety training on language models often induces over-refusal: improved safety on harmful prompts at the cost of increased refusal on harmless ones. Though this trade-off can be mi…
Reasoning as an Adaptive Defense for Safety
Taeyoun Kim, Fahim Tajwar, Aditi Raghunathan +1
Reasoning methods that adaptively allocate test-time compute have advanced LLM performance on easy to verify domains such as math and code. In this work, we study how to utilize th…
Mitigating Bias in RAG: Controlling the Embedder
Taeyoun Kim, Jacob Springer, Aditi Raghunathan +1
In retrieval augmented generation (RAG) systems, each individual component -- the LLM, embedder, and corpus -- could introduce biases in the form of skews towards outputting certai…
Predicting the Performance of Foundation Models via Agreement-on-the-Line
Rahul Saxena, Taeyoun Kim, Aman Mehra +3
Estimating the out-of-distribution performance in regimes where labels are scarce is critical to safely deploy foundation models. Recently, it was shown that ensembles of neural ne…
Testing the Limits of Jailbreaking Defenses with the Purple Problem
Taeyoun Kim, Suhas Kotha, Aditi Raghunathan
The rise of "jailbreak" attacks on language models has led to a flurry of defenses aimed at preventing undesirable responses. We critically examine the two stages of the defense pi…