Papers by Prithviraj Ammanabrolu
InSCIt: Information-Seeking Conversations with Mixed-Initiative Interactions (2023.tacl-1)
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Zeqiu Wu, Ryu Parish, Hao Cheng, Sewon Min, Prithviraj Ammanabrolu, Mari Ostendorf, Hannaneh Hajishirzi
| Challenge: | In information-seeking conversations, a user may ask questions that are under-specified or unanswerable. |
| Approach: | They present a dataset for information-seeking conversations with mixed-initiative interactions . they use Wikipedia to search for answers and provide relevant information . |
| Outcome: | The proposed system significantly underperforms humans in two of the most recent studies. |
I Cast Detect Thoughts: Learning to Converse and Guide with Intents and Theory-of-Mind in Dungeons and Dragons (2023.acl-long)
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Pei Zhou, Andrew Zhu, Jennifer Hu, Jay Pujara, Xiang Ren, Chris Callison-Burch, Yejin Choi, Prithviraj Ammanabrolu
| Challenge: | Existing dialogue agents, while able to produce human-like responses, often do not model goal-driven and grounded language interactions. |
| Approach: | They propose to decompose and model teacher-student natural language interactions into (1) the DM’s intent to guide players toward a given goal; (2) the dm’s guidance utterance to the players expressing this intent; (3) a theory-of-mind model that anticipates the players’ reaction to the guidance one turn into the future. |
| Outcome: | The proposed task is based on a goal-driven and grounded environment with a teacher-student interaction model and theory-of-mind model. |
Inference-Time Policy Adapters (IPA): Tailoring Extreme-Scale LMs without Fine-tuning (2023.emnlp-main)
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Ximing Lu, Faeze Brahman, Peter West, Jaehun Jung, Khyathi Chandu, Abhilasha Ravichander, Prithviraj Ammanabrolu, Liwei Jiang, Sahana Ramnath, Nouha Dziri, Jillian Fisher, Bill Lin, Skyler Hallinan, Lianhui Qin, Xiang Ren, Sean Welleck, Yejin Choi
| Challenge: | Extreme-scale language models have shown exceptional performance on a variety of language tasks, but the degree of control offered by these models through pure prompting is limited. |
| Approach: | They propose an inference-time policy adapter which tailors a large base model without fine-tuning it. |
| Outcome: | The proposed model outperforms baseline methods on five challenging text generation tasks and even over GPT-4. |
How to Motivate Your Dragon: Teaching Goal-Driven Agents to Speak and Act in Fantasy Worlds (2021.naacl-main)
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| Challenge: | a recent improvement in the quality of natural language processing and generation (NLG) is needed for goal-oriented ML driven agents. |
| Approach: | They propose a reinforcement learning system that integrates large-scale language modeling and commonsense reasoning-based pre-training to imbue the agent with relevant priors. |
| Outcome: | The proposed system is able to act and talk naturally with respect to their motivations. |
Aligning to Social Norms and Values in Interactive Narratives (2022.naacl-main)
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| Challenge: | Social value alignment is the ability to create agents that act in alignment with socially beneficial norms and values in interactive narratives or text-based games. |
| Approach: | They introduce a game-value ALignment agent that uses social commonsense to restrict its action space to actions that are aligned with socially beneficial values. |
| Outcome: | The proposed agent improves state-of-the-art task performance by 4% while reducing the frequency of socially harmful behaviors by 25% compared to strong contemporary value alignment approaches. |
Transfer in Deep Reinforcement Learning Using Knowledge Graphs (D19-53)
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| Challenge: | Text adventure games provide a stepping stone toward grounding action in language . prior work demonstrated that using a knowledge graph as a state representation facilitates faster control policy learning. |
| Approach: | They propose to use knowledge graphs as a representation for domain knowledge transfer for training text-adventure playing reinforcement learning agents. |
| Outcome: | The proposed methods let us learn a higher-quality control policy faster in text adventure games. |
Playing Text-Adventure Games with Graph-Based Deep Reinforcement Learning (N19-1)
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| Challenge: | Text adventure games provide a platform for exploring reinforcement learning in combinatorial action space, such as natural language. |
| Approach: | They propose a deep reinforcement learning architecture that represents the game state as a knowledge graph which is learned during exploration. |
| Outcome: | The proposed architecture can learn a control policy faster than baseline alternatives. |
Behavior Cloned Transformers are Neurosymbolic Reasoners (2023.eacl-main)
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| Challenge: | In injecting actions from symbolic modules into the action space of a behavior cloned transformer agent increases performance on four text game benchmarks that test arithmetic, navigation, sorting, and common sense reasoning by an average of 22% . contemporary agents struggle on tasks such as navigation, admetic and other tasks that humans make use of external tools. |
| Approach: | They propose to inject actions from symbolic modules into the action space of a behavior cloned transformer agent to increase performance on four text game benchmarks that test arithmetic, navigation, sorting, and common sense reasoning by an average of 22% . |
| Outcome: | The proposed method improves performance on four text game benchmarks that test arithmetic, navigation, sorting, and common sense reasoning by 22%, allowing an agent to reach the highest possible performance on unseen games. |
Situated Dialogue Learning through Procedural Environment Generation (2022.acl-long)
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| Challenge: | a key hypothesis in the pursuit towards creating goal-driven natural language-based agents is interactivity and environment grounding is critical for effective language learning. |
| Approach: | They augment LIGHT by learning to procedurally generate additional novel textual worlds and quests to create a curriculum of steadily increasing difficulty for training agents. |
| Outcome: | The authors augment LIGHT by learning to procedurally generate additional novel textual worlds and quests to create a curriculum of increasing difficulty for training agents to achieve such goals. |
ScienceWorld: Is your Agent Smarter than a 5th Grader? (2022.emnlp-main)
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| Challenge: | Existing models cannot reason about or explain learned science concepts in novel contexts, despite transformer-based progress in question-answering and scientific text processing . |
| Approach: | They propose a benchmark to test agents’ scientific reasoning abilities in a new interactive text environment at the level of a standard elementary school science curriculum. |
| Outcome: | The proposed model outperforms a model trained for 100k steps in a standard elementary school science curriculum. |