4 papers
Same Evidence, Different Answers: Canonical-Context On-Policy Distillation for Multi-Turn Language Models
Zizhuo Lin, Quanling Liu, Jinsheng Quan +6
Large language models (LLMs) often solve a task when all instructions are given in a single prompt, but fail when the same information is revealed gradually across turns. When a cl…
EmergentTTS-Eval: Evaluating TTS Models on Complex Prosodic, Expressiveness, and Linguistic Challenges Using Model-as-a-Judge
Ruskin Raj Manku, Yuzhi Tang, Xingjian Shi +2
Text-to-Speech (TTS) benchmarks often fail to capture how well models handle nuanced and semantically complex text. Building on , we introduce $\textit{Emerge…
L3Ms -- Lagrange Large Language Models
Guneet S. Dhillon, Xingjian Shi, Yee Whye Teh +1
Supervised fine-tuning (SFT) and alignment of large language models (LLMs) are key steps in providing a good user experience. However, the concept of an appropriate alignment is in…
RPGBENCH: Evaluating Large Language Models as Role-Playing Game Engines
Pengfei Yu, Dongming Shen, Silin Meng +8
We present RPGBench, the first benchmark designed to evaluate large language models (LLMs) as text-based role-playing game (RPG) engines. RPGBench comprises two core tasks: Game Cr…