Papers with human ability
Awakening Augmented Generation: Learning to Awaken Internal Knowledge of Large Language Models for Question Answering (2025.coling-main)
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| Challenge: | Recent studies indicate that Large Language Models model rich knowledge, but it is often not activated and awakened. |
| Approach: | They propose a framework that leverages richer context to enhance question answering . Explicit awakening fine-tunes a context generator to create a synthetic, compressed document that functions as symbolic context. |
| Outcome: | The proposed framework mimics the human ability to answer questions using only thinking and recalling to compensate for knowledge gaps. |
Meta-Reflection: A Feedback-Free Reflection Learning Framework (2025.acl-long)
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Yaoke Wang, Yun Zhu, XintongBao XintongBao, Wenqiao Zhang, Suyang Dai, Kehan Chen, Wenqiang Li, Gang Huang, Siliang Tang, Yueting Zhuang
| Challenge: | Existing approaches to improve large language models' ability to understand and reason are limited by external feedback. |
| Approach: | They propose a feedback-free reflection mechanism that requires only a single inference pass without external feedback. |
| Outcome: | The proposed method is based on an industrial e-commerce benchmark and public datasets. |
Conditional and Modal Reasoning in Large Language Models (2024.emnlp-main)
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| Challenge: | Inferences involving conditionals and modals play a central role in the fundamental human ability to reason about distal possibilities. |
| Approach: | They propose to assess the extent to which 29 LLMs are able to distinguish logically correct inferences from logical fallacious ones. |
| Outcome: | The LLMs that make basic mistakes with conditionals and modals display inconsistent judgments across inference patterns involving epistemic modal and conditionals, and give answers about complex conditional inferences that do not match reported human judgments. |
Improving In-Context Learning with Prediction Feedback for Sentiment Analysis (2024.findings-acl)
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| Challenge: | Large language models (LLMs) have achieved promising results in sentiment analysis through the in-context learning paradigm. |
| Approach: | They propose a framework that incorporates prior predictions and feedback to improve sentiment understanding by incorporating prior feedback and leveraging a feedback-driven prompt. |
| Outcome: | The proposed framework improves on nine sentiment analysis datasets with an average improvement of 5.95% over conventional methods. |
Self-Evolving GPT: A Lifelong Autonomous Experiential Learner (2024.acl-long)
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| Challenge: | Existing approaches to provide LLMs with textual task-solving experience rely on manual efforts to acquire and apply such experience for each task. |
| Approach: | They propose a lifelong autonomous experiential learning framework based on LLMs that learns and accumulates experience through experience transfer and induction. |
| Outcome: | The proposed framework performs reliably in each intermediate step and improves GPT-3.5 and GPT-4 on widely used NLP datasets. |
CITB: A Benchmark for Continual Instruction Tuning (2023.findings-emnlp)
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| Challenge: | Existing methods for instruction tuning do not leverage the rich natural language instructions. |
| Approach: | They propose to use a benchmark to study how instruction tuning works in CL tasks. |
| Outcome: | The proposed method can achieve similar or better results than existing CL methods. |
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. |