7 papers
SymCode: A Neurosymbolic Approach to Mathematical Reasoning via Verifiable Code Generation
Sina Bagheri Nezhad, Yao Li, Ameeta Agrawal
Large Language Models (LLMs) often struggle with complex mathematical reasoning, where prose-based generation leads to unverified and arithmetically unsound solutions. Current prom…
Enhancing Large Language Models with Neurosymbolic Reasoning for Multilingual Tasks
Sina Bagheri Nezhad, Ameeta Agrawal
Large language models (LLMs) often struggle to perform multi-target reasoning in long-context scenarios where relevant information is scattered across extensive documents. To addre…
Fair Summarization: Bridging Quality and Diversity in Extractive Summaries
Sina Bagheri Nezhad, Sayan Bandyapadhyay, Ameeta Agrawal
Fairness in multi-document summarization of user-generated content remains a critical challenge in natural language processing (NLP). Existing summarization methods often fail to e…
The Impact of Model Scaling on Seen and Unseen Language Performance
Rhitabrat Pokharel, Sina Bagheri Nezhad, Ameeta Agrawal +1
The rapid advancement of Large Language Models (LLMs), particularly those trained on multilingual corpora, has intensified the need for a deeper understanding of their performance…
Beyond Data Quantity: Key Factors Driving Performance in Multilingual Language Models
Sina Bagheri Nezhad, Ameeta Agrawal, Rhitabrat Pokharel
Multilingual language models (MLLMs) are crucial for handling text across various languages, yet they often show performance disparities due to differences in resource availability…
Evaluating Multilingual Long-Context Models for Retrieval and Reasoning
Ameeta Agrawal, Andy Dang, Sina Bagheri Nezhad +2
Recent large language models (LLMs) demonstrate impressive capabilities in handling long contexts, some exhibiting near-perfect recall on synthetic retrieval tasks. However, these…