4 papers
The Imperfective Paradox Is Not Necessarily in Large Language Models: A Benchmark Failure Before a Model Failure
Kaiqiao Han, Yizhou Sun
The imperfective paradox provides a useful test of compositional semantic analysis. Recent work constructs an NLI benchmark and reports that models frequently infer completed telic…
Structure-Aware RAG: Structured Retrieval Augmented Generation from Noisy Data for Conversational Agents
Kaiqiao Han, LuAn Tang, Renliang Sun +6
Large Language Models (LLMs) have been widely adopted in conversational applications. However, their reliance on parametric knowledge limits reliability in real-world scenarios tha…
Policy Frameworks for Transparent Chain-of-Thought Reasoning in Large Language Models
Yihang Chen, Haikang Deng, Kaiqiao Han +1
Chain-of-Thought (CoT) reasoning enhances large language models (LLMs) by decomposing complex problems into step-by-step solutions, improving performance on reasoning tasks. Howeve…
Concept-Reversed Winograd Schema Challenge: Evaluating and Improving Robust Reasoning in Large Language Models via Abstraction
Kaiqiao Han, Tianqing Fang, Zhaowei Wang +2
While Large Language Models (LLMs) have showcased remarkable proficiency in reasoning, there is still a concern about hallucinations and unreliable reasoning issues due to semantic…