1 citations · 1 across the 4 of their papers we have counts for
11 papers
Improving the Trade-off Between Watermark Strength and Speculative Sampling Efficiency for Language Models
Weiqing He, Xiang Li, Li Shen +2
Watermarking is a principled approach for tracing the provenance of large language model (LLM) outputs, but its deployment in practice is hindered by inference inefficiency. Specul…
Optimal Detection for Language Watermarks with Pseudorandom Collision
T. Tony Cai, Xiang Li, Qi Long +2
Text watermarking plays a crucial role in ensuring the traceability and accountability of large language model (LLM) outputs and mitigating misuse. While promising, most existing m…
On the Empirical Power of Goodness-of-Fit Tests in Watermark Detection
Weiqing He, Xiang Li, Tianqi Shang +3
Large language models (LLMs) raise concerns about content authenticity and integrity because they can generate human-like text at scale. Text watermarks, which embed detectable sta…
Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory
Jiancong Xiao, Zhekun Shi, Kaizhao Liu +2
Despite its empirical success, Reinforcement Learning from Human Feedback (RLHF) has been shown to violate almost all the fundamental axioms in social choice theory -- such as majo…
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know?
Xiang Li, Jiayi Xin, Qi Long +1
Accurate evaluation of large language models (LLMs) is crucial for understanding their capabilities and guiding their development. However, current evaluations often inconsistently…
Fundamental Limits of Game-Theoretic LLM Alignment: Smith Consistency and Preference Matching
Zhekun Shi, Kaizhao Liu, Qi Long +2
Nash Learning from Human Feedback is a game-theoretic framework for aligning large language models (LLMs) with human preferences by modeling learning as a two-player zero-sum game.…