221 citations · 483 across the 30 of their papers we have counts for
8 papers · 1 filter
Program-Aided Reasoners (better) Know What They Know
Anubha Kabra, Sanketh Rangreji, Yash Mathur +3
Prior work shows that program-aided reasoning, in which large language models (LLMs) are combined with programs written in programming languages such as Python, can significantly i…
How FaR Are Large Language Models From Agents with Theory-of-Mind?
Pei Zhou, Aman Madaan, Srividya Pranavi Potharaju +9
"Thinking is for Doing." Humans can infer other people's mental states from observations--an ability called Theory-of-Mind (ToM)--and subsequently act pragmatically on those infere…
AutoMix: Automatically Mixing Language Models
Pranjal Aggarwal, Aman Madaan, Ankit Anand +10
Large language models (LLMs) are now available from cloud API providers in various sizes and configurations. While this diversity offers a broad spectrum of choices, effectively le…
Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language Generation
Patrick Fernandes, Aman Madaan, Emmy Liu +8
Many recent advances in natural language generation have been fueled by training large language models on internet-scale data. However, this paradigm can lead to models that genera…
Let's Sample Step by Step: Adaptive-Consistency for Efficient Reasoning and Coding with LLMs
Pranjal Aggarwal, Aman Madaan, Yiming Yang +1
A popular approach for improving the correctness of output from large language models (LLMs) is Self-Consistency - poll the LLM multiple times and output the most frequent solution…
RL4F: Generating Natural Language Feedback with Reinforcement Learning for Repairing Model Outputs
Afra Feyza Akyürek, Ekin Akyürek, Aman Madaan +4
Despite their unprecedented success, even the largest language models make mistakes. Similar to how humans learn and improve using feedback, previous work proposed providing langua…
Self-Refine: Iterative Refinement with Self-Feedback
Aman Madaan, Niket Tandon, Prakhar Gupta +13
Like humans, large language models (LLMs) do not always generate the best output on their first try. Motivated by how humans refine their written text, we introduce Self-Refine, an…
Learning Performance-Improving Code Edits
Alexander Shypula, Aman Madaan, Yimeng Zeng +7
With the decline of Moore's law, optimizing program performance has become a major focus of software research. However, high-level optimizations such as API and algorithm changes r…