221 citations · 262 across the 9 of their papers we have counts for
9 papers
Olmo 3
Team Olmo, :, Allyson Ettinger +66
We introduce Olmo 3, a family of state-of-the-art, fully-open language models at the 7B and 32B parameter scales. Olmo 3 model construction targets long-context reasoning, function…
SUPER: Evaluating Agents on Setting Up and Executing Tasks from Research Repositories
Ben Bogin, Kejuan Yang, Shashank Gupta +5
Given that Large Language Models (LLMs) have made significant progress in writing code, can they now be used to autonomously reproduce results from research repositories? Such a ca…
AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents
Harsh Trivedi, Tushar Khot, Mareike Hartmann +6
Autonomous agents that address day-to-day digital tasks (e.g., ordering groceries for a household), must not only operate multiple apps (e.g., notes, messaging, shopping app) via A…
LLM-SR: Scientific Equation Discovery via Programming with Large Language Models
Parshin Shojaee, Kazem Meidani, Shashank Gupta +2
Mathematical equations have been unreasonably effective in describing complex natural phenomena across various scientific disciplines. However, discovering such insightful equation…
Bias Runs Deep: Implicit Reasoning Biases in Persona-Assigned LLMs
Shashank Gupta, Vaishnavi Shrivastava, Ameet Deshpande +4
Recent works have showcased the ability of LLMs to embody diverse personas in their responses, exemplified by prompts like 'You are Yoda. Explain the Theory of Relativity.' While t…
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…
Sparsely Activated Mixture-of-Experts are Robust Multi-Task Learners
Shashank Gupta, Subhabrata Mukherjee, Krishan Subudhi +4
Traditional multi-task learning (MTL) methods use dense networks that use the same set of shared weights across several different tasks. This often creates interference where two o…
Knowledge Infused Decoding
Ruibo Liu, Guoqing Zheng, Shashank Gupta +5
Pre-trained language models (LMs) have been shown to memorize a substantial amount of knowledge from the pre-training corpora; however, they are still limited in recalling factuall…
Exploring Low-Cost Transformer Model Compression for Large-Scale Commercial Reply Suggestions
Vaishnavi Shrivastava, Radhika Gaonkar, Shashank Gupta +1
Fine-tuning pre-trained language models improves the quality of commercial reply suggestion systems, but at the cost of unsustainable training times. Popular training time reductio…