Papers with PF
Towards a Progression-Aware Autonomous Dialogue Agent (2022.naacl-main)
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| Challenge: | Recent advances in large-scale language modeling and generation have enabled the creation of dialogue agents that exhibit human-like responses in a wide range of conversational scenarios. |
| Approach: | They propose a framework in which dialogue agents can evaluate the progression of a conversation toward or away from desired outcomes and use this signal to inform planning for subsequent responses. |
| Outcome: | The proposed framework evaluates the progression of a conversation toward or away from desired outcomes and uses this signal to inform planning for subsequent responses. |
Representation Degeneration Problem in Prompt-based Models for Natural Language Understanding (2024.lrec-main)
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| Challenge: | Prompt-based fine-tuning (PF) models have shown improved performance on few-shot natural language understanding benchmarks. |
| Approach: | They propose a framework to alleviate anisotropy in the embedding space by aligning with pre-trained language models' training objective. |
| Outcome: | The proposed method outperforms mainstream methods on many NLU benchmarks. |
Towards the Pedagogical Steering of Large Language Models for Tutoring: A Case Study with Modeling Productive Failure (2025.findings-acl)
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| Challenge: | Recent studies have shown that LLMs are not able to provide one-to-one tutoring solutions because of their high cost and efficiency. |
| Approach: | They propose an algorithm to optimize LLM prompts and steer it to follow a predefined multi-turn tutoring plan represented as a transition graph. |
| Outcome: | The proposed algorithm is able to optimize LLM prompts and steer it to follow a predefined multi-turn tutoring plan represented as a transition graph. |