9 citations · 11 across the 14 of their papers we have counts for
15 papers
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…
Gender Bias in MT for a Genderless Language: New Benchmarks for Basque
Amaia Murillo, Olatz-Perez-de-Viñaspre, Naiara Perez
Large language models (LLMs) and machine translation (MT) systems are increasingly used in our daily lives, but their outputs can reproduce gender bias present in the training data…
An Empirical Study on Noisy Data and LLM Pretraining Loss Divergence
Qizhen Zhang, Ankush Garg, Jakob Foerster +3
Large-scale pretraining datasets drive the success of large language models (LLMs). However, these web-scale corpora inevitably contain large amounts of noisy data due to unregulat…
Balancing Knowledge Delivery and Emotional Comfort in Healthcare Conversational Systems
Shang-Chi Tsai, Yun-Nung Chen
With the advancement of large language models, many dialogue systems are now capable of providing reasonable and informative responses to patients' medical conditions. However, whe…
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…
Jailbreak Distillation: Renewable Safety Benchmarking
Jingyu Zhang, Ahmed Elgohary, Xiawei Wang +5
Large language models (LLMs) are rapidly deployed in critical applications, raising urgent needs for robust safety benchmarking. We propose Jailbreak Distillation (JBDistill), a no…
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…
Compute Optimal Scaling of Skills: Knowledge vs Reasoning
Nicholas Roberts, Niladri Chatterji, Sharan Narang +2
Scaling laws are a critical component of the LLM development pipeline, most famously as a way to forecast training decisions such as 'compute-optimally' trading-off parameter count…
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…
BTS: Harmonizing Specialized Experts into a Generalist LLM
Qizhen Zhang, Prajjwal Bhargava, Chloe Bi +9
We present Branch-Train-Stitch (BTS), an efficient and flexible training algorithm for combining independently trained large language model (LLM) experts into a single, capable gen…
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…
Law of the Weakest Link: Cross Capabilities of Large Language Models
Ming Zhong, Aston Zhang, Xuewei Wang +14
The development and evaluation of Large Language Models (LLMs) have largely focused on individual capabilities. However, this overlooks the intersection of multiple abilities acros…
The Llama 3 Herd of Models
Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri +556
Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models th…
Effective Long-Context Scaling of Foundation Models
Wenhan Xiong, Jingyu Liu, Igor Molybog +18
We present a series of long-context LLMs that support effective context windows of up to 32,768 tokens. Our model series are built through continual pretraining from Llama 2 with l…