4 citations · 10 across the 16 of their papers we have counts for
14 papers · 1 filter
CT: Counterfactual Causal Reasoning for Sentiment Shifts in Social-Media Conversation Trees
S M Rafiuddin, Atriya Sen
Sentiment in social-media threads does not only vary across posts; it shifts as users react to claims, corrections, evidence, and hostility within a branching reply tree. We study…
HorizonBench: Long-Horizon Personalization with Evolving Preferences
Shuyue Stella Li, Bhargavi Paranjape, Kerem Oktar +9
User preferences evolve across months of interaction, and tracking them requires inferring when a stated preference has been changed by a subsequent life event. We define this prob…
From KMMLU-Redux to KMMLU-Pro: A Professional Korean Benchmark Suite for LLM Evaluation
Seokhee Hong, Sunkyoung Kim, Guijin Son +3
The development of Large Language Models (LLMs) requires robust benchmarks that encompass not only academic domains but also industrial fields to effectively evaluate their applica…
Dialect Normalization using Large Language Models and Morphological Rules
Antonios Dimakis, John Pavlopoulos, Antonios Anastasopoulos
Natural language understanding systems struggle with low-resource languages, including many dialects of high-resource ones. Dialect-to-standard normalization attempts to tackle thi…
ZeroSumEval: An Extensible Framework For Scaling LLM Evaluation with Inter-Model Competition
Hisham A. Alyahya, Haidar Khan, Yazeed Alnumay +2
We introduce ZeroSumEval, a dynamic, competition-based, and evolving evaluation framework for Large Language Models (LLMs) that leverages competitive games. ZeroSumEval encompasses…
Multilingual Machine Translation with Open Large Language Models at Practical Scale: An Empirical Study
Menglong Cui, Pengzhi Gao, Wei Liu +2
Large language models (LLMs) have shown continuously improving multilingual capabilities, and even small-scale open-source models have demonstrated rapid performance enhancement. I…
Optimizing Pretraining Data Mixtures with LLM-Estimated Utility
William Held, Bhargavi Paranjape, Punit Singh Koura +3
Large Language Models improve with increasing amounts of high-quality training data. However, leveraging larger datasets requires balancing quality, quantity, and diversity across…
LLäMmlein: Transparent, Compact and Competitive German-Only Language Models from Scratch
Jan Pfister, Julia Wunderle, Andreas Hotho
We create two German-only decoder models, LLäMmlein 120M and 1B, transparently from scratch and publish them, along with the training data, for the German NLP research community to…
Prompting Contrastive Explanations for Commonsense Reasoning Tasks
Bhargavi Paranjape, Julian Michael, Marjan Ghazvininejad +2
Many commonsense reasoning NLP tasks involve choosing between one or more possible answers to a question or prompt based on knowledge that is often implicit. Large pretrained langu…
EASE: Extractive-Abstractive Summarization with Explanations
Haoran Li, Arash Einolghozati, Srinivasan Iyer +4
Current abstractive summarization systems outperform their extractive counterparts, but their widespread adoption is inhibited by the inherent lack of interpretability. To achieve…
FiD-Ex: Improving Sequence-to-Sequence Models for Extractive Rationale Generation
Kushal Lakhotia, Bhargavi Paranjape, Asish Ghoshal +3
Natural language (NL) explanations of model predictions are gaining popularity as a means to understand and verify decisions made by large black-box pre-trained models, for NLP tas…
An Information Bottleneck Approach for Controlling Conciseness in Rationale Extraction
Bhargavi Paranjape, Mandar Joshi, John Thickstun +2
Decisions of complex language understanding models can be rationalized by limiting their inputs to a relevant subsequence of the original text. A rationale should be as concise as…
Entity Projection via Machine Translation for Cross-Lingual NER
Alankar Jain, Bhargavi Paranjape, Zachary C. Lipton
Although over 100 languages are supported by strong off-the-shelf machine translation systems, only a subset of them possess large annotated corpora for named entity recognition. M…
Weighted Global Normalization for Multiple Choice Reading Comprehension over Long Documents
Aditi Chaudhary, Bhargavi Paranjape, Michiel de Jong
Motivated by recent evidence pointing out the fragility of high-performing span prediction models, we direct our attention to multiple choice reading comprehension. In particular,…