6 papers
DeepFact: Co-Evolving Benchmarks and Agents for Deep Research Factuality
Yukun Huang, Leonardo F. R. Ribeiro, Momchil Hardalov +3
Search-augmented LLM agents can produce deep research reports (DRRs), but verifying claim-level factuality remains challenging. Existing fact-checkers are primarily designed for ge…
Cite Pretrain: Retrieval-Free Knowledge Attribution for Large Language Models
Yukun Huang, Sanxing Chen, Jian Pei +2
Trustworthy language models should provide both correct and verifiable answers. However, citations generated directly by standalone LLMs are often unreliable. As a result, current…
When Greedy Wins: Emergent Exploitation Bias in Meta-Bandit LLM Training
Sanxing Chen, Xiaoyin Chen, Yukun Huang +2
While Large Language Models (LLMs) hold promise to become autonomous agents, they often explore suboptimally in sequential decision-making. Recent work has sought to enhance this c…
Real-time Factuality Assessment from Adversarial Feedback
Sanxing Chen, Yukun Huang, Bhuwan Dhingra
We show that existing evaluations for assessing the factuality of news from conventional sources, such as claims on fact-checking websites, result in high accuracies over time for…
Fuzzy Speculative Decoding for a Tunable Accuracy-Runtime Tradeoff
Maximilian Holsman, Yukun Huang, Bhuwan Dhingra
Speculative Decoding (SD) enforces strict distributional equivalence to the target model when accepting candidate tokens. While it maintains the target model's generation quality,…
To Trust or Not to Trust? Enhancing Large Language Models' Situated Faithfulness to External Contexts
Yukun Huang, Sanxing Chen, Hongyi Cai +1
Large Language Models (LLMs) are often augmented with external contexts, such as those used in retrieval-augmented generation (RAG). However, these contexts can be inaccurate or in…