97 citations · 342 across the 16 of their papers we have counts for
11 papers · 1 filter
Spontaneous Reward Hacking in Iterative Self-Refinement
Jane Pan, He He, Samuel R. Bowman +1
Language models are capable of iteratively improving their outputs based on natural language feedback, thus enabling in-context optimization of user preference. In place of human u…
Steering Without Side Effects: Improving Post-Deployment Control of Language Models
Asa Cooper Stickland, Alexander Lyzhov, Jacob Pfau +2
Language models (LMs) have been shown to behave unexpectedly post-deployment. For example, new jailbreaks continually arise, allowing model misuse, despite extensive red-teaming an…
Let's Think Dot by Dot: Hidden Computation in Transformer Language Models
Jacob Pfau, William Merrill, Samuel R. Bowman
Chain-of-thought responses from language models improve performance across most benchmarks. However, it remains unclear to what extent these performance gains can be attributed to…
LLM Evaluators Recognize and Favor Their Own Generations
Arjun Panickssery, Samuel R. Bowman, Shi Feng
Self-evaluation using large language models (LLMs) has proven valuable not only in benchmarking but also methods like reward modeling, constitutional AI, and self-refinement. But n…
Question Decomposition Improves the Faithfulness of Model-Generated Reasoning
Ansh Radhakrishnan, Karina Nguyen, Anna Chen +21
As large language models (LLMs) perform more difficult tasks, it becomes harder to verify the correctness and safety of their behavior. One approach to help with this issue is to p…
Eight Things to Know about Large Language Models
Samuel R. Bowman
The widespread public deployment of large language models (LLMs) in recent months has prompted a wave of new attention and engagement from advocates, policymakers, and scholars fro…