7 papers
Rethinking Speech Codecs: From Compression to Autoregressive Generative Modeling
Yazheng Yang, Yao Qiu, Hui Su +1
Recent advances in speech language models leverage discrete speech representations from pretrained codecs to enable scalable training and generation. However, existing codecs are p…
KGCaRe: Explainable Complex Conditional Question Answering using Automatic Knowledge Graph Construction and Context Retrieval with LLMs
Ghanshyam Verma, Simanta Sarkar, Devishree Pillai +6
Answering complex conditional questions using Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) remains a challenge, particularly in domain-specific contexts wh…
LongCat-Next: Lexicalizing Modalities as Discrete Tokens
Meituan LongCat Team, Bin Xiao, Chao Wang +86
The prevailing Next-Token Prediction (NTP) paradigm has driven the success of large language models through discrete autoregressive modeling. However, contemporary multimodal syste…
Harnessing the Power of Reinforcement Learning for Language-Model-Based Information Retriever via Query-Document Co-Augmentation
Jingming Liu, Yumeng Li, Wei Shi +3
Recent studies have proposed leveraging Large Language Models (LLMs) as information retrievers through query rewriting. However, for challenging corpora, we argue that enhancing qu…
Investigating and Enhancing the Robustness of Large Multimodal Models Against Temporal Inconsistency
Jiafeng Liang, Shixin Jiang, Xuan Dong +7
Large Multimodal Models (LMMs) have recently demonstrated impressive performance on general video comprehension benchmarks. Nevertheless, for broader applications, the robustness o…
Detecting Errors through Ensembling Prompts (DEEP): An End-to-End LLM Framework for Detecting Factual Errors
Alex Chandler, Devesh Surve, Hui Su
Accurate text summarization is one of the most common and important tasks performed by Large Language Models, where the costs of human review for an entire document may be high, bu…
Unraveling the Mystery of Scaling Laws: Part I
Hui Su, Zhi Tian, Xiaoyu Shen +1
Scaling law principles indicate a power-law correlation between loss and variables such as model size, dataset size, and computational resources utilized during training. These pri…