6 papers · 1 filter
AcquisitionSynthesis: Targeted Data Generation using Acquisition Functions
Ishika Agarwal, Sofia Stoica, Emre Can Acikgoz +4
Data quality remains a critical bottleneck in developing capable, competitive models. Researchers have explored many ways to generate top quality samples. Some works rely on reject…
Language Specific Knowledge: Do Models Know Better in X than in English?
Ishika Agarwal, Nimet Beyza Bozdag, Nisval Patel +1
Often, multilingual language models are trained with the objective to map semantically similar content (in different languages) in the same latent space. In this paper, we show a n…
A Rising Tide Lifts All Boats: MTQE Rewards for Idioms Improve General Translation Quality
Ishika Agarwal, Zhenlin He, Dhruva Patil +1
Non-compositional expressions (e.g., idioms, proverbs, and metaphors) pose significant challenges for neural machine translation systems because their meanings cannot be derived fr…
Tree-of-Debate: Multi-Persona Debate Trees Elicit Critical Thinking for Scientific Comparative Analysis
Priyanka Kargupta, Ishika Agarwal, Tal August +1
With the exponential growth of research facilitated by modern technology and improved accessibility, scientific discoveries have become increasingly fragmented within and across fi…
DELIFT: Data Efficient Language model Instruction Fine Tuning
Ishika Agarwal, Krishnateja Killamsetty, Lucian Popa +1
Fine-tuning large language models (LLMs) is essential for enhancing their performance on specific tasks but is often resource-intensive due to redundant or uninformative data. To a…
Instruct, Not Assist: LLM-based Multi-Turn Planning and Hierarchical Questioning for Socratic Code Debugging
Priyanka Kargupta, Ishika Agarwal, Dilek Hakkani-Tur +1
Socratic questioning is an effective teaching strategy, encouraging critical thinking and problem-solving. The conversational capabilities of large language models (LLMs) show grea…