Publications (12)
Prompted Opinion Summarization with GPT-3.5
Adithya Bhaskar, Alexander R. Fabbri, Greg Durrett
Large language models have shown impressive performance across a wide variety of tasks, including text summarization. In this paper, we show that this strong performance extends to…
Unintentional Unalignment: Likelihood Displacement in Direct Preference Optimization
Noam Razin, Sadhika Malladi, Adithya Bhaskar +3
Direct Preference Optimization (DPO) and its variants are increasingly used for aligning language models with human preferences. Although these methods are designed to teach a mode…
The Heuristic Core: Understanding Subnetwork Generalization in Pretrained Language Models
Adithya Bhaskar, Dan Friedman, Danqi Chen
Prior work has found that pretrained language models (LMs) fine-tuned with different random seeds can achieve similar in-domain performance but generalize differently on tests of s…
Improving Language Understanding from Screenshots
Tianyu Gao, Zirui Wang, Adithya Bhaskar +1
An emerging family of language models (LMs), capable of processing both text and images within a single visual view, has the promise to unlock complex tasks such as chart understan…
Benchmarking and Improving Text-to-SQL Generation under Ambiguity
Adithya Bhaskar, Tushar Tomar, Ashutosh Sathe +1
Research in Text-to-SQL conversion has been largely benchmarked against datasets where each text query corresponds to one correct SQL. However, natural language queries over real-l…
Cache Me If You Can: How Many KVs Do You Need for Effective Long-Context LMs?
Adithya Bhaskar, Alexander Wettig, Tianyu Gao +2
Language models handle increasingly long contexts for tasks such as book summarization, but this leads to growing memory costs for the key-value (KV) cache. Many prior works have p…