5 citations · 10 across the 13 of their papers we have counts for
4 papers · 1 filter
Muse Glimmer 30B: An On-Device Agentic Model
Arash Ebrahim Abtahizadeh, Muna Aghamelu, Adil Ahmed +327
We introduce Muse Glimmer, an open-source 30B parameter causal language model with perception encoder, purpose-built for autonomous agentic tasks on consumer hardware. Muse Glimmer…
Learning Auxiliary Tasks Improves Reference-Free Hallucination Detection in Open-Domain Long-Form Generation
Chengwei Qin, Wenxuan Zhou, Karthik Abinav Sankararaman +10
Hallucination, the generation of factually incorrect information, remains a significant challenge for large language models (LLMs), especially in open-domain long-form generation.…
Multi-IF: Benchmarking LLMs on Multi-Turn and Multilingual Instructions Following
Yun He, Di Jin, Chaoqi Wang +16
Large Language Models (LLMs) have demonstrated impressive capabilities in various tasks, including instruction following, which is crucial for aligning model outputs with user expe…
Improving Model Factuality with Fine-grained Critique-based Evaluator
Yiqing Xie, Wenxuan Zhou, Pradyot Prakash +9
Factuality evaluation aims to detect factual errors produced by language models (LMs) and hence guide the development of more factual models. Towards this goal, we train a factuali…