6 citations · 9 across the 10 of their papers we have counts for
9 papers · 1 filter
Cross-Lingual Activation Steering for Multilingual Language Models
Rhitabrat Pokharel, Ameeta Agrawal, Tanay Nagar
Large language models exhibit strong multilingual capabilities, yet significant performance gaps persist between dominant and non-dominant languages. Prior work attributes this gap…
From Policy to Logic for Efficient and Interpretable Coverage Assessment
Rhitabrat Pokharel, Hamid Reza Hassanzadeh, Ameeta Agrawal
Large Language Models (LLMs) have demonstrated strong capabilities in interpreting lengthy, complex legal and policy language. However, their reliability can be undermined by hallu…
MTQ-Eval: Multilingual Text Quality Evaluation for Language Models
Rhitabrat Pokharel, Ameeta Agrawal
The use of large language models (LLMs) for evaluating outputs is becoming an increasingly effective and scalable approach. However, it remains uncertain whether this capability ex…
CAPO: Confidence Aware Preference Optimization Learning for Multilingual Preferences
Rhitabrat Pokharel, Yufei Tao, Ameeta Agrawal
Preference optimization is a critical post-training technique used to align large language models (LLMs) with human preferences, typically by fine-tuning on ranked response pairs.…
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…
Generating Continuations in Multilingual Idiomatic Contexts
Rhitabrat Pokharel, Ameeta Agrawal
The ability to process idiomatic or literal multiword expressions is a crucial aspect of understanding and generating any language. The task of generating contextually relevant con…
Estimating Semantic Similarity between In-Domain and Out-of-Domain Samples
Rhitabrat Pokharel, Ameeta Agrawal
Prior work typically describes out-of-domain (OOD) or out-of-distribution (OODist) samples as those that originate from dataset(s) or source(s) different from the training set but…