8 papers · 1 filter
Executable Schema Contracts: From Automatic Ingestion to Multi-Source Retrieval
Padmaja Jonnalagedda, Yuguang Yao, Xiang Gao +2
Real-world data spans tables, documents, and semi-structured files with implicit semantics. Querying this data requires integrating evidence across inconsistent schemas and formats…
When Evidence is Sparse: Weakly Supervised Early Failure Alerting in Dialogs and LLM-Agent Trajectories
Avinash Baidya, Xinran Liang, Ruocheng Guo +2
Early failure alerting requires deciding, while a dialog or agent trajectory is still unfolding, whether to flag it as likely to fail. This is challenging because supervision is ty…
RIMRULE: Improving Tool-Using Language Agents via MDL-Guided Rule Learning
Xiang Gao, Yuguang Yao, Qi Zhang +5
Large language models (LLMs) often struggle to use tools reliably in domain-specific settings, where APIs may be idiosyncratic, under-documented, or tailored to private workflows.…
The Behavior Gap: Evaluating Zero-shot LLM Agents in Complex Task-Oriented Dialogs
Avinash Baidya, Kamalika Das, Xiang Gao
Large Language Model (LLM)-based agents have significantly impacted Task-Oriented Dialog Systems (TODS) but continue to face notable performance challenges, especially in zero-shot…
Learning to Search Effective Example Sequences for In-Context Learning
Xiang Gao, Ankita Sinha, Kamalika Das
Large language models (LLMs) demonstrate impressive few-shot learning capabilities, but their performance varies widely based on the sequence of in-context examples. Key factors in…
Gradient-guided Attention Map Editing: Towards Efficient Contextual Hallucination Mitigation
Yu Wang, Kamalika Das, Xiang Gao +3
In tasks like summarization and open-book question answering (QA), Large Language Models (LLMs) often encounter "contextual hallucination", where they produce irrelevant or incorre…