2 citations · 3 across the 5 of their papers we have counts for
6 papers
Learning to Reason in 13 Parameters
John X. Morris, Niloofar Mireshghallah, Mark Ibrahim +1
Recent research has shown that language models can learn to \textit{reason}, often via reinforcement learning. Some work even trains low-rank parameterizations for reasoning, but c…
ReasonCACHE: Teaching LLMs To Reason Without Weight Updates
Sharut Gupta, Phillip Isola, Stefanie Jegelka +4
Can Large language models (LLMs) learn to reason without any weight update and only through in-context learning (ICL)? ICL is strikingly sample-efficient, often learning from only…
AbstentionBench: Reasoning LLMs Fail on Unanswerable Questions
Polina Kirichenko, Mark Ibrahim, Kamalika Chaudhuri +1
For Large Language Models (LLMs) to be reliably deployed in both everyday and high-stakes domains, knowing when not to answer is equally critical as answering correctly. Real-world…
TituLLMs: A Family of Bangla LLMs with Comprehensive Benchmarking
Shahriar Kabir Nahin, Rabindra Nath Nandi, Sagor Sarker +7
In this paper, we present TituLLMs, the first large pretrained Bangla LLMs, available in 1b and 3b parameter sizes. Due to computational constraints during both training and infere…
Uncertainty-Based Abstention in LLMs Improves Safety and Reduces Hallucinations
Christian Tomani, Kamalika Chaudhuri, Ivan Evtimov +2
A major barrier towards the practical deployment of large language models (LLMs) is their lack of reliability. Three situations where this is particularly apparent are correctness,…
WorldSense: A Synthetic Benchmark for Grounded Reasoning in Large Language Models
Youssef Benchekroun, Megi Dervishi, Mark Ibrahim +7
We propose WorldSense, a benchmark designed to assess the extent to which LLMs are consistently able to sustain tacit world models, by testing how they draw simple inferences from…