98 citations · 365 across the 15 of their papers we have counts for
15 papers
TokenSwap: Benchmarking and Reducing the Modality Gap in Multimodal LLMs
Andong Hua, Colton Bishop, Igor Mordatch +5
Multimodal large language models (MLLMs) should exhibit cross-modal consistency, generating consistent responses given semantically equivalent inputs across modalities. However, we…
Multiagent Finetuning: Self Improvement with Diverse Reasoning Chains
Vighnesh Subramaniam, Yilun Du, Joshua B. Tenenbaum +3
Large language models (LLMs) have achieved remarkable performance in recent years but are fundamentally limited by the underlying training data. To improve models beyond the traini…
Improving Factuality and Reasoning in Language Models through Multiagent Debate
Yilun Du, Shuang Li, Antonio Torralba +2
Large language models (LLMs) have demonstrated remarkable capabilities in language generation, understanding, and few-shot learning in recent years. An extensive body of work has e…
Composing Ensembles of Pre-trained Models via Iterative Consensus
Shuang Li, Yilun Du, Joshua B. Tenenbaum +2
Large pre-trained models exhibit distinct and complementary capabilities dependent on the data they are trained on. Language models such as GPT-3 are capable of textual reasoning b…
Learning Iterative Reasoning through Energy Minimization
Yilun Du, Shuang Li, Joshua B. Tenenbaum +1
Deep learning has excelled on complex pattern recognition tasks such as image classification and object recognition. However, it struggles with tasks requiring nontrivial reasoning…
Pre-Trained Language Models for Interactive Decision-Making
Shuang Li, Xavier Puig, Chris Paxton +11
Language model (LM) pre-training is useful in many language processing tasks. But can pre-trained LMs be further leveraged for more general machine learning problems? We propose an…