33 citations · 61 across the 9 of their papers we have counts for
8 papers · 1 filter
NATURAL PLAN: Benchmarking LLMs on Natural Language Planning
Huaixiu Steven Zheng, Swaroop Mishra, Hugh Zhang +8
We introduce NATURAL PLAN, a realistic planning benchmark in natural language containing 3 key tasks: Trip Planning, Meeting Planning, and Calendar Scheduling. We focus our evaluat…
In-Context Principle Learning from Mistakes
Tianjun Zhang, Aman Madaan, Luyu Gao +5
In-context learning (ICL, also known as few-shot prompting) has been the standard method of adapting LLMs to downstream tasks, by learning from a few input-output examples. Nonethe…
Instruction-Following Evaluation for Large Language Models
Jeffrey Zhou, Tianjian Lu, Swaroop Mishra +5
One core capability of Large Language Models (LLMs) is to follow natural language instructions. However, the evaluation of such abilities is not standardized: Human evaluations are…
InstructExcel: A Benchmark for Natural Language Instruction in Excel
Justin Payan, Swaroop Mishra, Mukul Singh +7
With the evolution of Large Language Models (LLMs) we can solve increasingly more complex NLP tasks across various domains, including spreadsheets. This work investigates whether L…
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…
Instruction Tuned Models are Quick Learners
Himanshu Gupta, Saurabh Arjun Sawant, Swaroop Mishra +4
Instruction tuning of language models has demonstrated the ability to enhance model generalization to unseen tasks via in-context learning using a few examples. However, typical su…