Papers by Larry Heck
Dialogue Learning with Human Teaching and Feedback in End-to-End Trainable Task-Oriented Dialogue Systems (N18-1)
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| Challenge: | Existing methods for learning task-oriented dialogues include applying reinforcement learning with user feedback on supervised pre-training models. |
| Approach: | They propose a hybrid imitation and reinforcement learning method that integrates user feedback and reinforcement training to improve the agent's performance. |
| Outcome: | The proposed method can learn from the mistake it makes via imitation learning from user teaching and feedback. |
Dense Passage Retrieval: Is it Retrieving? (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) internally store repositories of knowledge, but access to these repositoriels is imprecise. |
| Approach: | They propose a paradigm called retrieval augmented generation to address hallucinations . they analyze the role of fine-tuning pre-trained networks to enhance alignment . |
| Outcome: | The proposed paradigm addresses hallucinations by fine-tuning pre-trained models . the model can be decentralized, inject facts as decentralized representations . |
Grounding Open-Domain Instructions to Automate Web Support Tasks (2021.naacl-main)
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| Challenge: | RUSS is a task and dataset to ground natural language instructions on the web to perform previously unseen tasks. |
| Approach: | They build a task and dataset to ground AI agents from open-domain, step-by-step instructions on the web. |
| Outcome: | The proposed model outperforms existing models that map instructions to actions without WebLang. |
UNLEARN Efficient Removal of Knowledge in Large Language Models (2025.findings-naacl)
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| Challenge: | Large Language Models excel in many tasks but are outperformed by specialized tools for certain tasks. |
| Approach: | They propose a method that uses subspace techniques to selectively remove knowledge . they propose 'unlearn' method that can forget or unlear the knowledge without retraining . |
| Outcome: | The proposed method outperforms existing methods for forgetting target knowledge while preserving related knowledge. |
mForms : Multimodal Form Filling with Question Answering (2024.lrec-main)
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| Challenge: | The paper presents a new approach to form-filling by reformulating the task as multimodal natural language Question Answering (QA). |
| Approach: | They propose a new approach to form-filling by reformulating the task as multimodal natural language Question Answering (QA) the paper introduces a multimodal form-filled dataset and an extension of the popular ATIS dataset to support future research and experimentation. |
| Outcome: | The proposed approach maintains robust accuracy for sparse training conditions and achieves state-of-the-art F1 of 0.97 on ATIS with approximately 1/10th the training data. |