5 papers
SHAPE of Thinking: Mathematical Heuristics for Analyzing and Improving LLM Reasoning
Jonghyun Song, Sangjun Song, Minjae Oh +3
Large language models (LLMs) achieve strong performance on mathematical reasoning benchmarks, yet final-answer accuracy reveals little about the strategies they use. We introduce \…
Don't Adapt Small Language Models for Tools; Adapt Tool Schemas to the Models
Jonggeun Lee, Woojung Song, Jongwook Han +2
Small language models (SLMs) enable scalable tool-augmented multi-agent systems where multiple SLMs handle subtasks orchestrated by a powerful coordinator. However, they struggle w…
Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions
Yoonah Park, Haesung Pyun, Yohan Jo
While large language models (LLMs) perform strongly on diverse tasks, their trustworthiness is limited by erratic behavior that is unfaithful to their internal knowledge. In partic…
Improving Dialogue State Tracking through Combinatorial Search for In-Context Examples
Haesung Pyun, Yoonah Park, Yohan Jo
In dialogue state tracking (DST), in-context learning comprises a retriever that selects labeled dialogues as in-context examples and a DST model that uses these examples to infer…
Multi-View Zero-Shot Open Intent Induction from Dialogues: Multi Domain Batch and Proxy Gradient Transfer
Hyukhun Koh, Haesung Pyun, Nakyeong Yang +1
In Task Oriented Dialogue (TOD) system, detecting and inducing new intents are two main challenges to apply the system in the real world. In this paper, we suggest the semantic mul…