23 citations · 26 across the 7 of their papers we have counts for
8 papers
HyperMCTS: Hypergraph-Augmented MCTS for Long-Horizon LLM Agents
Tingsong Xiao, Nithish Balachandar Moudhgalya, Chandrayee Basu +5
Long-horizon tasks require large language model (LLM) agents to coordinate decisions under constraints that span an entire solution. Monte Carlo Tree Search (MCTS) offers a promisi…
PA3: Policy-Aware Agent Alignment through Chain-of-Thought
Shubhashis Roy Dipta, Daniel Bis, Kun Zhou +4
Conversational assistants powered by large language models (LLMs) excel at tool-use tasks but struggle with adhering to complex, business-specific rules. While models can reason ov…
Know When You're Wrong: Aligning Confidence with Correctness for LLM Error Detection
Xie Xiaohu, Liu Xiaohu, Yao Benjamin
As large language models (LLMs) are increasingly deployed in critical decision-making systems, the lack of reliable methods to measure their uncertainty presents a fundamental trus…
Beyond Perfect APIs: A Comprehensive Evaluation of LLM Agents Under Real-World API Complexity
Doyoung Kim, Zhiwei Ren, Jie Hao +11
We introduce WildAGTEval, a benchmark designed to evaluate large language model (LLM) agents' function-calling capabilities under realistic API complexity. Unlike prior work that a…
PersonaPKT: Building Personalized Dialogue Agents via Parameter-efficient Knowledge Transfer
Xu Han, Bin Guo, Yoon Jung +4
Personalized dialogue agents (DAs) powered by large pre-trained language models (PLMs) often rely on explicit persona descriptions to maintain personality consistency. However, suc…
KEPLET: Knowledge-Enhanced Pretrained Language Model with Topic Entity Awareness
Yichuan Li, Jialong Han, Kyumin Lee +3
In recent years, Pre-trained Language Models (PLMs) have shown their superiority by pre-training on unstructured text corpus and then fine-tuning on downstream tasks. On entity-ric…
Large-scale Hybrid Approach for Predicting User Satisfaction with Conversational Agents
Dookun Park, Hao Yuan, Dongmin Kim +10
Measuring user satisfaction level is a challenging task, and a critical component in developing large-scale conversational agent systems serving the needs of real users. An widely…
Knowledge Distillation from Internal Representations
Gustavo Aguilar, Yuan Ling, Yu Zhang +3
Knowledge distillation is typically conducted by training a small model (the student) to mimic a large and cumbersome model (the teacher). The idea is to compress the knowledge fro…