180 citations · 286 across the 91 of their papers we have counts for
28 papers · 1 filter
CharacterBench: Benchmarking Character Customization of Large Language Models
Jinfeng Zhou, Yongkang Huang, Bosi Wen +13
Character-based dialogue (aka role-playing) enables users to freely customize characters for interaction, which often relies on LLMs, raising the need to evaluate LLMs' character c…
Does RLHF Scale? Exploring the Impacts From Data, Model, and Method
Zhenyu Hou, Pengfan Du, Yilin Niu +7
This study explores the scaling properties of Reinforcement Learning from Human Feedback (RLHF) in Large Language Models (LLMs). Although RLHF is considered an important step in po…
Agent-SafetyBench: Evaluating the Safety of LLM Agents
Zhexin Zhang, Shiyao Cui, Yida Lu +4
As large language models (LLMs) are increasingly deployed as agents, their integration into interactive environments and tool use introduce new safety challenges beyond those assoc…
SPaR: Self-Play with Tree-Search Refinement to Improve Instruction-Following in Large Language Models
Jiale Cheng, Xiao Liu, Cunxiang Wang +7
Instruction-following is a fundamental capability of language models, requiring the model to recognize even the most subtle requirements in the instructions and accurately reflect…
Unveiling User Satisfaction and Creator Productivity Trade-Offs in Recommendation Platforms
Fan Yao, Yiming Liao, Jingzhou Liu +4
On User-Generated Content (UGC) platforms, recommendation algorithms significantly impact creators' motivation to produce content as they compete for algorithmically allocated user…
RecFlow: An Industrial Full Flow Recommendation Dataset
Qi Liu, Kai Zheng, Rui Huang +15
Industrial recommendation systems (RS) rely on the multi-stage pipeline to balance effectiveness and efficiency when delivering items from a vast corpus to users. Existing RS bench…
Unlocking Reasoning Potential in Large Langauge Models by Scaling Code-form Planning
Jiaxin Wen, Jian Guan, Hongning Wang +2
Despite the remarkable success of large language models (LLMs) on traditional natural language processing tasks, their planning ability remains a critical bottleneck in tackling co…
Data Selection via Optimal Control for Language Models
Yuxian Gu, Li Dong, Hongning Wang +4
This work investigates the selection of high-quality pre-training data from massive corpora to enhance LMs' capabilities for downstream usage. We formulate data selection as a gene…
LogicGame: Benchmarking Rule-Based Reasoning Abilities of Large Language Models
Jiayi Gui, Yiming Liu, Jiale Cheng +6
Large Language Models (LLMs) have demonstrated notable capabilities across various tasks, showcasing complex problem-solving abilities. Understanding and executing complex rules, a…
ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools
Team GLM, :, Aohan Zeng +56
We introduce ChatGLM, an evolving family of large language models that we have been developing over time. This report primarily focuses on the GLM-4 language series, which includes…
Benchmarking Complex Instruction-Following with Multiple Constraints Composition
Bosi Wen, Pei Ke, Xiaotao Gu +11
Instruction following is one of the fundamental capabilities of large language models (LLMs). As the ability of LLMs is constantly improving, they have been increasingly applied to…
From Theft to Bomb-Making: The Ripple Effect of Unlearning in Defending Against Jailbreak Attacks
Zhexin Zhang, Junxiao Yang, Yida Lu +5
Large Language Models (LLMs) are known to be vulnerable to jailbreak attacks. An important observation is that, while different types of jailbreak attacks can generate significantl…
AutoDetect: Towards a Unified Framework for Automated Weakness Detection in Large Language Models
Jiale Cheng, Yida Lu, Xiaotao Gu +6
Although Large Language Models (LLMs) are becoming increasingly powerful, they still exhibit significant but subtle weaknesses, such as mistakes in instruction-following or coding…
Knowledge-to-Jailbreak: Investigating Knowledge-driven Jailbreaking Attacks for Large Language Models
Shangqing Tu, Zhuoran Pan, Wenxuan Wang +6
Large language models (LLMs) have been increasingly applied to various domains, which triggers increasing concerns about LLMs' safety on specialized domains, e.g. medicine. Despite…
Learning Task Decomposition to Assist Humans in Competitive Programming
Jiaxin Wen, Ruiqi Zhong, Pei Ke +3
When using language models (LMs) to solve complex problems, humans might struggle to understand the LM-generated solutions and repair the flawed ones. To assist humans in repairing…
Full Stage Learning to Rank: A Unified Framework for Multi-Stage Systems
Kai Zheng, Haijun Zhao, Rui Huang +6
The Probability Ranking Principle (PRP) has been considered as the foundational standard in the design of information retrieval (IR) systems. The principle requires an IR module's…
Improve Temporal Awareness of LLMs for Sequential Recommendation
Zhendong Chu, Zichao Wang, Ruiyi Zhang +3
Large language models (LLMs) have demonstrated impressive zero-shot abilities in solving a wide range of general-purpose tasks. However, it is empirically found that LLMs fall shor…
User Welfare Optimization in Recommender Systems with Competing Content Creators
Fan Yao, Yiming Liao, Mingzhe Wu +6
Driven by the new economic opportunities created by the creator economy, an increasing number of content creators rely on and compete for revenue generated from online content reco…
ChatGLM-RLHF: Practices of Aligning Large Language Models with Human Feedback
Zhenyu Hou, Yilin Niu, Zhengxiao Du +8
ChatGLM is a free-to-use AI service powered by the ChatGLM family of large language models (LLMs). In this paper, we present the ChatGLM-RLHF pipeline -- a reinforcement learning f…
Overcoming Reward Overoptimization via Adversarial Policy Optimization with Lightweight Uncertainty Estimation
Xiaoying Zhang, Jean-Francois Ton, Wei Shen +2
We introduce Adversarial Policy Optimization (AdvPO), a novel solution to the pervasive issue of reward over-optimization in Reinforcement Learning from Human Feedback (RLHF) for L…
Federated Linear Contextual Bandits with Heterogeneous Clients
Ethan Blaser, Chuanhao Li, Hongning Wang
The demand for collaborative and private bandit learning across multiple agents is surging due to the growing quantity of data generated from distributed systems. Federated bandit…
Human vs. Generative AI in Content Creation Competition: Symbiosis or Conflict?
Fan Yao, Chuanhao Li, Denis Nekipelov +2
The advent of generative AI (GenAI) technology produces transformative impact on the content creation landscape, offering alternative approaches to produce diverse, high-quality co…
Stealthy Adversarial Attacks on Stochastic Multi-Armed Bandits
Zhiwei Wang, Huazheng Wang, Hongning Wang
Adversarial attacks against stochastic multi-armed bandit (MAB) algorithms have been extensively studied in the literature. In this work, we focus on reward poisoning attacks and f…
Incentivized Truthful Communication for Federated Bandits
Zhepei Wei, Chuanhao Li, Tianze Ren +2
To enhance the efficiency and practicality of federated bandit learning, recent advances have introduced incentives to motivate communication among clients, where a client particip…