16 citations · 20 across the 21 of their papers we have counts for
12 papers · 1 filter
NeoHorse-1: Towards Recursive Self-Improvement via Agentic Post-Training with Routing Harness
NeoHorse Team, Guoliang Cao, Guohao Dai +34
Recursive self-improvement (RSI) requires a concrete mechanism through which an AI system observes its capabilities and converts that evidence into the next round of learning. We p…
Verilog-Evolve: Feedback-Driven and Skill-Evolving Verilog Generation
Zehua Pei, Hui-Ling Zhen, Yu Zhang +3
Large language models (LLMs) have improved Verilog generation from natural-language specifications, but most pipelines still treat generation as isolated sampling followed by funct…
FocuSFT: Bilevel Optimization for Dilution-Aware Long-Context Fine-Tuning
Zehua Pei, Hui-Ling Zhen, Xianzhi Yu +3
Large language models can now process increasingly long inputs, yet their ability to effectively use information spread across long contexts remains limited. We trace this gap to h…
MemDLM: Memory-Enhanced DLM Training
Zehua Pei, Hui-Ling Zhen, Weizhe Lin +4
Diffusion Language Models (DLMs) offer attractive advantages over Auto-Regressive (AR) models, such as full-attention parallel decoding and flexible generation. However, standard D…
Beyond Speedup -- Utilizing KV Cache for Sampling and Reasoning
Zeyu Xing, Xing Li, Hui-Ling Zhen +2
KV caches, typically used only to speed up autoregressive decoding, encode contextual information that can be reused for downstream tasks at no extra cost. We propose treating the…
Reinforcing Compositional Retrieval: Retrieving Step-by-Step for Composing Informative Contexts
Quanyu Long, Jianda Chen, Zhengyuan Liu +3
Large Language Models (LLMs) have demonstrated remarkable capabilities across numerous tasks, yet they often rely on external context to handle complex tasks. While retrieval-augme…
Large Language Models Know What Makes Exemplary Contexts
Quanyu Long, Jianda Chen, Wenya Wang +1
In-context learning (ICL) has proven to be a significant capability with the advancement of Large Language models (LLMs). By instructing LLMs using few-shot demonstrative examples,…
Does In-Context Learning Really Learn? Rethinking How Large Language Models Respond and Solve Tasks via In-Context Learning
Quanyu Long, Yin Wu, Wenya Wang +1
In-context Learning (ICL) has emerged as a powerful capability alongside the development of scaled-up large language models (LLMs). By instructing LLMs using few-shot demonstrative…
Backdoor Attacks on Dense Retrieval via Public and Unintentional Triggers
Quanyu Long, Yue Deng, LeiLei Gan +2
Dense retrieval systems have been widely used in various NLP applications. However, their vulnerabilities to potential attacks have been underexplored. This paper investigates a no…
Adapt in Contexts: Retrieval-Augmented Domain Adaptation via In-Context Learning
Quanyu Long, Wenya Wang, Sinno Jialin Pan
Large language models (LLMs) have showcased their capability with few-shot inference known as in-context learning. However, in-domain demonstrations are not always readily availabl…
SOUL: Towards Sentiment and Opinion Understanding of Language
Yue Deng, Wenxuan Zhang, Sinno Jialin Pan +1
Sentiment analysis is a well-established natural language processing task, with sentiment polarity classification being one of its most popular and representative tasks. However, d…
Multilingual Jailbreak Challenges in Large Language Models
Yue Deng, Wenxuan Zhang, Sinno Jialin Pan +1
While large language models (LLMs) exhibit remarkable capabilities across a wide range of tasks, they pose potential safety concerns, such as the ``jailbreak'' problem, wherein mal…