4 citations · 7 across the 10 of their papers we have counts for
11 papers
EgoMem: Lifelong Memory Agent for Full-duplex Omnimodal Models
Yiqun Yao, Naitong Yu, Xiang Li +7
We introduce EgoMem, the first lifelong memory agent tailored for full-duplex models that process real-time omnimodal streams. EgoMem enables real-time models to recognize multiple…
FLM-Audio: Natural Monologues Improves Native Full-Duplex Chatbots via Dual Training
Yiqun Yao, Xiang Li, Xin Jiang +5
Full-duplex dialog models aim to listen and speak simultaneously, delivering rapid responses to dynamic user input. Among different solutions to full-duplexity, a native solution m…
RoboEgo System Card: An Omnimodal Model with Native Full Duplexity
Yiqun Yao, Xiang Li, Xin Jiang +4
Humans naturally process real-world multimodal information in a full-duplex manner. In artificial intelligence, replicating this capability is essential for advancing model develop…
If an LLM Were a Character, Would It Know Its Own Story? Evaluating Lifelong Learning in LLMs
Siqi Fan, Xiusheng Huang, Yiqun Yao +6
Large language models (LLMs) can carry out human-like dialogue, but unlike humans, they are stateless due to the superposition property. However, during multi-turn, multi-agent int…
Position-Aware Depth Decay Decoding (): Boosting Large Language Model Inference Efficiency
Siqi Fan, Xuezhi Fang, Xingrun Xing +3
Due to the large number of parameters, the inference phase of Large Language Models (LLMs) is resource-intensive. Unlike traditional model compression, which needs retraining, rece…
Sketch: A Toolkit for Streamlining LLM Operations
Xin Jiang, Xiang Li, Wenjia Ma +8
Large language models (LLMs) represented by GPT family have achieved remarkable success. The characteristics of LLMs lie in their ability to accommodate a wide range of tasks throu…
Open-domain Implicit Format Control for Large Language Model Generation
Yiqun Yao, Wenjia Ma, Xuezhi Fang +7
Controlling the format of outputs generated by large language models (LLMs) is a critical functionality in various applications. Current methods typically employ constrained decodi…
52B to 1T: Lessons Learned via Tele-FLM Series
Xiang Li, Yiqun Yao, Xin Jiang +17
Large Language Models (LLMs) represent a significant stride toward Artificial General Intelligence. As scaling laws underscore the potential of increasing model sizes, the academic…
Tele-FLM Technical Report
Xiang Li, Yiqun Yao, Xin Jiang +17
Large language models (LLMs) have showcased profound capabilities in language understanding and generation, facilitating a wide array of applications. However, there is a notable p…
Quantifying and Attributing the Hallucination of Large Language Models via Association Analysis
Li Du, Yequan Wang, Xingrun Xing +4
Although demonstrating superb performance on various NLP tasks, large language models (LLMs) still suffer from the hallucination problem, which threatens the reliability of LLMs. T…
FLM-101B: An Open LLM and How to Train It with $100K Budget
Xiang Li, Yiqun Yao, Xin Jiang +10
Large language models (LLMs) are considered important approaches towards foundational machine intelligence, achieving remarkable success in Natural Language Processing and multimod…