Papers with CoAT
CoAT: Chain-of-Associated-Thoughts Framework for Enhancing Large Language Models Reasoning (2025.findings-emnlp)
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| Challenge: | OpenAI-o1 enables ‘slow thinking’ because it is closer to the human thought process . |
| Approach: | They propose a new framework that integrates the Monte Carlo Tree Search algorithm and a dynamic mechanism for integrating new key information, termed ‘associative memory’. |
| Outcome: | The proposed framework improves performance on open-source multi-hop reasoning datasets and more than 15% gain on proprietary CRB dataset. |
Android in the Zoo: Chain-of-Action-Thought for GUI Agents (2024.findings-emnlp)
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| Challenge: | Existing studies on large language models (LLMs) focus on the semantics of smartphone operations. |
| Approach: | They propose a large language model (LLM) which predicts a sequence of actions of API by analyzing past actions and visual observations. |
| Outcome: | The proposed model improves the prediction of actions on a zero-shot Android-In-The-Zoo dataset compared to previous models . |
Concept-aware Data Construction Improves In-context Learning of Language Models (2024.findings-acl)
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| Challenge: | Recent work curating in-context learners assumes that ICL emerges from vast over-parametrization or the scale of multitask training. |
| Approach: | They propose a framework for constructing training scenarios that make it beneficial for the LM to learn to utilize the analogical reasoning concepts from demonstrations. |
| Outcome: | The proposed framework makes it beneficial for the LM to learn to utilize the analogical reasoning concepts from demonstrations and fares comparably to previous in-context learners trained in large-scale multitask learning requiring magnitudes of more training data. |